The DOI matches its Crossref record, with a small difference in title or year; 89 of the 89 references it cites were found in a registry or an index; 14 of the 144 sources its claims cite were checked for support.
Sentiment Analysis in Tourism: Capitalizing on Big Data
Open accessAbstract
Advances in technology have fundamentally changed how information is produced and consumed by all actors involved in tourism. Tourists can now access different sources of information, and they can generate their own content and share their views and experiences. Tourism content shared through social media has become a very influential information source that impacts tourism in terms of both reputation and performance. However, the volume of data on the Internet has reached a level that makes manual processing almost impossible, demanding new analytical approaches. Sentiment analysis is rapidly emerging as an automated process of examining semantic relationships and meaning in reviews. In this article, different sentiment analysis approaches applied in tourism are reviewed and assessed in terms of the datasets used and performances on key evaluation metrics. The article concludes by outlining future research avenues to further advance sentiment analysis in tourism as part of a broader Big Data approach.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This review examines sentiment analysis methods used in tourism, assesses their datasets and performance, and argues that sentiment analysis is an early step toward broader Big Data approaches in tourism research. It finds that machine learning and dictionary-based methods dominate, hotel/accommodation reviews are the most common data source, and multi-class or neutral sentiment detection remains difficult.
Key findings
- Tourism sentiment studies rely heavily on hotel and accommodation reviews, with fewer studies on restaurants and airlines; common sources include TripAdvisor, Booking, Ctrip, and Twitter. Reported dataset sizes vary widely, from 479 Chinese hotel reviews in Zheng and Ye to 417,170 hotel reviews in Brob, and 10,000,000 tweets in Shimada et al. with 200,000 or 116 annotated items depending on the task.
- Binary classification generally performs better than three-class classification. Examples include Zheng and Ye’s SVM on hotel reviews at 0.912 accuracy, Zhang et al.’s Naïve Bayes on restaurant reviews at 0.957 accuracy, and Shimada et al.’s Naïve Bayes on tourism information at 0.92 accuracy; three-class results are lower, such as Kasper and Vela at 0.81 accuracy, Schmunk et al. at 0.768, and Bucur at 0.72.
- On the Sanders Twitter dataset, Levallois’s method gave the best binary results (accuracy 0.82, F-measure 0.82), while VADER gave the best multi-class results (accuracy 0.60, F-measure 0.58).
- The review reports that machine learning approaches are most common, dictionary-based approaches are also frequent, and hybrid approaches are rare; domain-specific lexicons are seldom used, and neutral reviews are especially hard to detect.
Methods
- The paper is a critical review of sentiment analysis in tourism. Studies were identified through Google keyword searches and by extracting tourism-related references from recent sentiment analysis reviews, rather than through a systematic Scopus or Web of Science search.
- It provides a technical overview of sentiment analysis steps—data retrieval, extraction and selection, pre-processing, feature extraction, topic detection, and data mining—and categorizes methods as supervised machine learning, unsupervised machine learning, dictionary/rule-based, semantic, and hybrid.
- It compares reported tourism studies using accuracy, precision, recall, and F-measure, and additionally evaluates three Twitter-oriented methods on a publicly available Twitter dataset.
Limitations
- Stated in the paper: results across tourism studies are not directly comparable because database sizes, numbers of classes, and data types differ; the search strategy was not a systematic database search; domain-specific lexicons are rarely used, which compromises quality; neutral reviews are difficult to detect; and hybrid methods have been used only in a few cases.
- Stated in the paper: some dictionary-based tourism studies were restricted by small keyword sets or failure to handle negation, limiting their findings.
- Evident from the text: many studies in the summary tables do not report key dataset or performance details, and the review does not conduct a meta-analysis or standardized re-evaluation of all included tourism methods.
Model-written summary. Check it against the paper before relying on it.
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Claims the paper makes
27 No full text of the sources · 25 Not checked yet · 25 No basis given · 19 Sources not found · 13 Own interpretation · 5 Partly supported · 4 Supported · 2 Supported so far · 2 Couldn’t confirm · 1 Own result · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Therefore, social media and blogs nowadays complements opinions attained from relatives, friends, colleagues, and official sources (Cantallops & Salvi, 2014;Chua & Banerjee, 2013;Filieri et al., 2015;Hepburn, 2007;Mack et al., 2008).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > 2.
What the sources say
- Not checked — no full text of the sourcecites Cantallops & Salvi, 2014 source
- Not matchedcites Chua & Banerjee, 2013
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Partly supportedcites Filieri et al., 2015 sourceExperimental — accuracy not yet measured
Section 2 says travelers use online reviews and treats consumer-generated content as important alongside officially provided information. It does not establish that social media and blogs complement opinions from relatives, friends, and colleagues; section 3.4.2 instead discusses travelers sharing online-review tips with friends. (LLM, conf=0.79)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“Integration of multiple Big Data sources, e.g., heterogeneous data sources, in the form of structured and unstructured data, such as customer feedback, reservation and booking data, and web search / navigation data, in customer and supplier sides (Höpken et al., 2013, Höpken et al., 2015), may reveal new insights that were not able to be detected with traditional approaches.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > 2.
What the sources say
- Not checked — no full text of the sourcecites Höpken et al., 2013 source
- Supportedcites Höpken et al., 2015 source
The Introduction says the paper integrates heterogeneous data across tourism business processes to extract previously unknown and unavailable knowledge, overcoming limitations of earlier approaches. The Data availability section identifies structured and unstructured sources, including booking records, web-navigation logs and customer feedback. (LLM, conf=0.93)
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“Whilst highly relevant for tourism, sentiment analysis in tourism is only beginning to gain in popularity (Feldman, 2013, Gao et al., 2015, Ribeiro et al., 2016).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Introduction
What the sources say
- Not checked — no full text of the sourcecites Feldman, 2013 source
- Not checked — no full text of the sourcecites Gao et al., 2015 source
- Could not verifycites Ribeiro et al., 2016 sourceExperimental — accuracy not yet measured
The Introduction discusses growing interest in sentiment analysis generally, and the Concluding remarks describe its use on social-network content. Neither addresses whether sentiment analysis in tourism was only beginning to gain popularity; searches for tourism, travel, and hotel applications found no relevant passage. Our copy therefore cannot verify the tourism-specific claim. (LLM, conf=0.77)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“The method proposed by Levallois, (2013) has provided the best results in the binary classification problem.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Performance of sentiment analyses in tourism studies
What the sources say
- Could not verifycites Levallois, 2013 sourceExperimental — accuracy not yet measured
The Accuracy section reports results for a three-class task—positive, negative, or neutral—and says another participant performed better on that task. Our copy does not report a binary-classification comparison, so it cannot establish whether Levallois’s method produced the best binary results. (LLM, conf=0.75)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Could not verify
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“However, the VADER (Hutto and Gilbert, 2014) delivered the best results in the multi-class classification case.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Performance of sentiment analyses in tourism studies
What the sources say
- Partly supportedcites Hutto and Gilbert, 2014 sourceExperimental — accuracy not yet measured
The Introduction and Results report strong three-class classification performance on tweets: VADER achieved F1 = 0.96, above individual human raters’ F1 = 0.84. But the Results say VADER performed “as well or better” than machine-learning approaches in three of four domain comparisons, not that it delivered the best multiclass results across the board. No page numbers are available in our copy. (LLM, conf=0.83)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“It appears that neutral reviews are difficult to detect in most of the sentiment analysis methods (Ribeiro et al., 2016).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Synthesis of the tourism-specific results
What the sources say
- Partly supportedcites Ribeiro et al., 2016 sourceExperimental — accuracy not yet measured
Section 5.3 says neutral messages were harder for most methods to detect, supporting the general point. But its review datasets have only two-class results, and Section 5.1 says product reviews were expected to contain polarized opinions; the paper does not establish this finding specifically for neutral reviews. No page number is available in our copy. (LLM, conf=0.88)
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
None of the sources cited for this claim have been checked yet.
In the paper
“It is also noted that most of the datasets used in the literature relate to hotel accommodation (e.g., Kasper &Vela, 2011 and2012;Tan & Wu, 2011;Bjorkelund et al., 2012;Gräbner et al., 2012;Bucur 2015;Marrese-Taylora et al., 2013;Markopoulos et al., 2015;Rossetti et al., 2015).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Identified studies and datasets used
What the sources say
- Not matchedcites Kasper &Vela, 2011 and2012
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites Tan & Wu, 2011 source
- Not matchedcites Bjorkelund et al., 2012
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Not matched
None of the sources cited for this claim have been checked yet.
In the paper
“The results indicate that the majority of tourism-related sentiment analysis studies used a binary polarity classification (e.g., Zheng & Ye, 2009;Ye et al., 2009;Gindl et al., 2010;Shimada et al., 2011;Zhang et al., 2011;Bjorkelund et al., 2012;Kang et al., 2012).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Performance of sentiment analyses in tourism studies
What the sources say
- Not checked — no full text of the sourcecites Zheng & Ye, 2009 source
- Not matchedcites Ye et al., 2009
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites Gindl et al., 2010
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“There is some indication, however, that better results in terms of Accuracy and F-measure were obtained when only two classes (Positive, Negative) were used in the experimentations.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Performance of sentiment analyses in tourism studies
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In most cases, publicly available dictionaries were used or adapted to the tourism context.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Table 2 here
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Probably not a substantial claim. 73
- It may state two separate claims as one. 26
Every source the paper cites for this claim was checked and supports it.
In the paper
“Umigon is another dictionary -based method, which uses a lexicon with heuristics for sentiment detection in Twitter reviews (Levallois, 2013).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Figure 2. here
What the sources say
- Supportedcites Levallois, 2013 source
The General principle of operation section identifies Umigon as a lexicon-based sentiment classifier designed for tweets and says it applies heuristics to lexicon matches. The Focus on lexicons and heuristics section describes its four sentiment-analysis lexicons. The paper calls the inputs tweets rather than “Twitter reviews,” but that wording does not change the method attributed to it. (LLM, conf=0.92)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“In the data mining process, different types of sentiment analysis methods can be distinguished in the literature; namely (i) machine learning, (ii) rule/dictionary-based and (iii) hybrid approaches (Feldman, 2013;Ribeiro et al., 2016).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Sentiment analysis methods
What the sources say
- Not checked — no full text of the sourcecites Feldman, 2013 source
- Supportedcites Ribeiro et al., 2016 source
The Introduction distinguishes machine-learning methods from lexical methods that use predefined word lists. Table 1 also describes methods combining dictionaries or lexicons with machine learning, supporting the cited sentence’s hybrid category. The source does not use that three-part classification verbatim, but its descriptions support the distinction. (LLM, conf=0.88)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Neural network and deep learning models (Irsoy & Cardie, 2014;Socher et al., 2013) and k-nearest neighbour method (Schmunk et al., 2014) were also employed for semantic analysis in the literature.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Sentiment analysis methods
What the sources say
- Supportedcites Irsoy & Cardie, 2014 source
The Introduction and Conclusion describe applying deep recurrent neural networks to opinion-expression extraction, a form of semantic analysis. The k-nearest-neighbour part of the citing sentence is attributed to Schmunk et al., not to Irsoy and Cardie. (LLM, conf=0.91)
- Supportedcites Socher et al., 2013 source
The Socher et al. citation attaches to neural network and deep learning models, not the separately cited k-nearest-neighbour method. The Conclusion describes the paper’s Recursive Neural Tensor Network for sentence-level sentiment detection, and the Semantic Vector Spaces section discusses neural representations for semantic tasks. This supports the cited example of a neural model used for semantic analysis. (LLM, conf=0.91)
- Not matchedcites Schmunk et al., 2014
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Valence Aware Dictionary for Sentiment Reasoning (VADER) is a method that has provided promising results on Twitter data (Hutto & Gilbert, 2014).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Figure 2. here
What the sources say
- Supportedcites Hutto & Gilbert, 2014 source
The Introduction reports that VADER achieved an F1 score of 0.96 when classifying the sentiment of tweets, outperforming individual human raters (0.84). This supports the description of promising results on Twitter data. (LLM, conf=0.94)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“The VADER sentiment lexicon is composed of more than 7,000 items, along with their associated sentiment intensity measures, validated by humans and specifically adapted to sentiment in microblog-like contexts, such as Twitter (Hutto & Gilbert, 2014).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Figure 2. here
What the sources say
- Supportedcites Hutto & Gilbert, 2014 source
Section 3.1 states that the VADER lexicon contains just over 7,500 features with human-validated sentiment polarity and intensity scores. The Introduction and Conclusion describe the lexicon as specifically attuned to microblog-like contexts, and the Introduction discusses its performance on tweets. (LLM, conf=0.94)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Umigon provides additional semantic features, such as time or subjectivity (Levallois, 2013).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Figure 2. here
What the sources say
- Supportedcites Levallois, 2013 source
The Abstract explicitly says Umigon provides indications for additional semantic features in tweets, including time indications and markers of subjectivity. This directly supports the paraphrase. (LLM, conf=0.95)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Some studies followed a slightly different approach, whereby the sentiment analysis was divided into two subtasks, namely: (i) classifying sentences into objective and subjective sentences, and (ii) then determining the polarity of the subjective sentences (Marrese-Taylora et al., 2013;Riloff & Wiebe, 2003).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Performance of sentiment analyses in tourism studies
What the sources say
- Not matchedcites Marrese-Taylora et al., 2013
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Partly supportedcites Riloff & Wiebe, 2003 sourceExperimental — accuracy not yet measured
Section 2.1 (page unavailable) says the paper’s goal is to classify individual sentences as subjective or objective. It does not describe a second step that determines the polarity of those subjective sentences; its discussion of positive and negative classification concerns other work on reviews at the document level. (LLM, conf=0.88)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“None of the reviews to date focus on tourism.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > What is sentiment analysis?
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Different types of features, such as term frequency (TF) (Ye et al., 2009), term frequency-inverse document frequency (TF-IDF) (Shi & Li, 2011), stemmed word (Ganu et al., 2009), bag-ofwords (Markopoulos et al., 2015), information gain (IG) (Zheng & Ye, 2009), n-gram (Brob, 2013;Kang et al., 2012;Markopoulos et al., 2015;Zhang et al., 2011;Pablos et al., 2015) were proposed to characterise tourism”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Table 2 here
What the sources say
- Not matchedcites Ye et al., 2009
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites Shi & Li, 2011 source
- Not matchedcites Ganu et al., 2009
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Tourism researchers have typically used two types of online content for their sentiment analysis: reviews of tourism obtained from professional websites (e.g., TripAdvisor, Booking, and Ctrip) and social media posts (e.g., Twitter).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Identified studies and datasets used
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The Naïve Bayes sentiment classification approach was trained using automatically labelled data.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Table 2 here
What the sources say
- Not checked — no full text of the sourcecites Shimada et al., 2011 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Therefore, reviews that contained a smiley face, for example, were considered as positive and those with an angry face were classed as negative (Shimada et al., 2011).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Table 2 here
What the sources say
- Not checked — no full text of the sourcecites Shimada et al., 2011 source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“A small number of studies focus on restaurants (Ganu et al., 2009;Zhang et al., 2011) and airlines (Misopoulos et al., 2014).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Identified studies and datasets used
What the sources say
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“An unsupervised machine learning approach based on Naïve Bayes classifier was implemented by Shimada et al. (2011) to produce a sentiment analysis of tourism data at the sentence level.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Table 2 here
What the sources say
- Not checked — no full text of the sourcecites Shimada et al., 2011 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Both supervised and unsupervised machine learning, dictionary based, semantic and hybrid sentiment analysis approaches were used in the tourism literature.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Table 2 here
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Both types of sources usually contain short text.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Identified studies and datasets used
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Schmunk et al. (2014) further discussed a system to initially detect the subjectivity of a sentence by a dictionary-based method.Then, the classification of the sentence into positive or negative was performed using bigrams features along with a SVM classifier (Schmunk et al., 2014).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Table 2 here
What the sources say
- Not matchedcites Schmunk et al., 2014
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“Earlier work by Waldhör and Rind (2008) proposes to combine a linguistic parsing methodology with information and terminology extraction methods in order to determine sentiment polarity of online blog reviews.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Table 2 here
What the sources say
- Not checked — no full text of the sourcecites Waldhör and Rind, 2008 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Another sentence level hybrid sentiment analysis system was presented by Kasper &Vela (2011 and2012) in the context of German language hotel reviews.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Table 2 here
What the sources say
- Not matchedcites Kasper &Vela, 2011 and2012
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“The dictionary used in Gräbner et al.'s (2012) study was a hotel domain specific lexicon of semantically relevant words.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Table 2 here
What the sources say
- Not checked — no full text of the sourcecites Gräbner et al., 2012 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“Most studies have used data written in English for sentiment analysis, but few used reviews written in Chinese, Spanish and German (Zheng & Ye, 2009;Tan & Wu, 2011;Garcia et al, 2012;Kasper & Vela, 2011;Zhang et al., 2011).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Table 2 here
What the sources say
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The analysis was, however, based on a limited number of 20 keywords (10 positive and 10 negative), which posed a significant restriction to the findings from this research.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Table 2 here
What the sources say
- Not checked — no full text of the sourcecites Misopoulos et al., 2014 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“K-mean clustering techniques and statistical models based on probability distribution of reviews in sentiment space (Rossetti et al., 2015, Xiang et al., 2015b) were also employed on tourism data.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Table 2 here
What the sources say
- Not checked — no full text of the sourcecites Rossetti et al., 2015 source
- Not matchedcites Xiang et al., 2015b
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Another example can be found in Sharma et al. (2015) who analysed travel reviews at the sentence level using a lexicon based system.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Table 2 here
What the sources say
- Not matchedcites Sharma et al., 2015
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“Sentiment analysis on Chinese e-WOM was proposed by Chiu et al. (2015).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Table 2 here
What the sources say
- Not matchedcites Chiu et al., 2015
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In summary, a relatively broad range of studies exist in the domain of tourism, mainly in relation to hotels and accommodation.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Table 2 here
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, our review revealed that most tourism sentiment analyses are based on a machine learning approach, although a considerable number of studies have also used a dictionary-based approach.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Table 2 here
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Combining a supervised probabilistic model and a heuristic n-phrase rule was used to effectively obtain customer opinions about hotel attributes.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Table 2 here
What the sources say
- Not matchedcites Chiu et al., 2015
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“Moreover, negation was not incorporated in the system to accurately capture the meaning of opinions, such as”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Table 2 here
What the sources say
- Not checked — no full text of the sourcecites Misopoulos et al., 2014 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Xiang et al. (2015a) proposed semantic approach for text analysis to understand hotel guest experience and their satisfaction.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Table 2 here
What the sources say
- Not matchedcites Xiang et al., 2015a
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“The results revealed those aspects of the airline customer service where customers were dissatisfied, satisfied, or even delighted.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Table 2 here
What the sources say
- Not checked — no full text of the sourcecites Misopoulos et al., 2014 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Other dictionary-based analysis focused on hotel and restaurant customer reviews (Bucur, 2015;Gräbner et al., 2012;Marrese-Taylora et al., 2013;Schmunk et al., 2014).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Table 2 here
What the sources say
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Using binary choice keywords and a Naïve Bayes algorithm helped measure sentiment polarities of tweets related to different tourist destinations (Claster et al., 2010a;Claster et al., 2010b;Claster et al., 2013).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Table 2 here
What the sources say
- Not matchedcites Claster et al., 2010a
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites Claster et al., 2010b
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites Claster et al., 2013 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Based on the above insights, it can be noted that the majority of tourism sentiment analyses used a machine learning approach, often trained with small annotated datasets, as this process needs considerable human resources.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Synthesis of the tourism-specific results
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Satisfaction as a theoretical construct has been explored and discussed for a long time, and multiple instruments exist to operationalise and measure it (Wang, 2016).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Introduction
What the sources say
- Not matchedcites Wang, 2016
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
In the paper
“It is, thus, not surprising that tourism has been recognized as the number one sector in terms of online engagement (Mack et al., 2008).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Introduction
What the sources say
- Not checked — no full text of the sourcecites Mack et al., 2008 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Not surprisingly, the focus of previous research was on business strategy development, innovation and product development, and marketing campaigns (Ellion, 2007;Kuttainen et al., 2012;Pan et al., 2007).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Introduction
What the sources say
- Not matchedcites Ellion, 2007
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites Kuttainen et al., 2012
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites Pan et al., 2007 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
In the paper
“The four Vs of Big Data, namely volume (scale), variety (different types of data), velocity (high speed, and real time), and veracity (uncertainty, and validity) are particularly relevant in consumer research (IBM, nd)”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Introduction
What the sources say
- Not matchedcites IBM, nd
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
None of the sources cited for this claim have been checked yet.
In the paper
“In addition, travel is one of the dominant topics on social media, for example on Facebook and Twitter (Neidhardt et al., 2017;Travelmail Reporter, 2013).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Introduction
What the sources say
- Not checked — no full text of the sourcecites Neidhardt et al., 2017 source
- Not matchedcites Travelmail Reporter, 2013
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The volume of online data relevant to the tourism context is increasing exponentially.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > 2.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Methodologically, sentiment analysis represents a polarity classification problem.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Overview
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“Interviewer bias and cultural influence in answering particular questions are other known problems of survey-based approaches (Veal, 2006).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Introduction
What the sources say
- Not matchedcites Veal, 2006
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“Besides, sentiment can also be modelled by machines for automation, and integration across various applications (Choi et al., 2007;Rabanser & Ricci, 2005).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > 2.
What the sources say
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“TripAdvisor alone counts 350 million unique visitors per month on their website and generates over 320 million reviews that cover accommodations, restaurants, and attractions (TripAdvisor, 2016).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > 2.
What the sources say
- Not matchedcites TripAdvisor, 2016
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
None of the sources cited for this claim have been checked yet.
In the paper
“Text summarization, and text classification along with natural language processing (NLP) are earlier technologies used to facilitate information processing and data analysis (Cantallops & Salvi, 2014;Ghose et al., 2012;Pan et al., 2007;Stringam & Gerdes, 2010;Xiang et al., 2015a).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > 2.
What the sources say
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Online platforms represent a two-way avenue of producing and consuming information, and 'co-creating experiences' (Sigala, 2016).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > 2.
What the sources say
- Not checked — no full text of the sourcecites Sigala, 2016 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“As a result, new Internet technologies have empowered people who previously did not have a voice (Hepburn, 2007).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > 2.
What the sources say
- Not matchedcites Hepburn, 2007
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“Despite its popularity, sentiment analysis is still in its infancy compared to earlier technologies, such as data mining and text summarization (Pan et al., 2007).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > 2.
What the sources say
- Not checked — no full text of the sourcecites Pan et al., 2007 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This review argues that sentiment analysis can become an important tool in tourism research.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > 2.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
In the paper
“Tourism is part of the 'experience economy' and those involved in the travel industry are increasingly seeking to understand the emotional and experiential elements of tourist activities (Ma, Scott, Gao & Ding, 2017).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > 2.
What the sources say
- Not checked — no full text of the sourcecites Ma, Scott, Gao & Ding, 2017 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The most successful professional platforms in relation to travel and tourism are TripAdvisor, Expedia, VirtualTourist, and LonelyPlanet (Bjorkelund et al., 2012;Gretzel et al., 2007;Rabanser & Ricci, 2005).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > 2.
What the sources say
- Not matchedcites Bjorkelund et al., 2012
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites Gretzel et al., 2007
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites Rabanser & Ricci, 2005 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
None of the sources cited for this claim have been checked yet.
In the paper
“Whilst research on sentiment analysis goes back to the 1970, only recently it has received increasing attention from both researchers and practitioners (Brob, 2013;Pang et al., 2002).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > 2.
What the sources say
- Not matchedcites Brob, 2013
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites Pang et al., 2002 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Online data related to tourism activities are generated at such velocity that they outstrip the potential of traditional (paper and pen) surveys to capture events in real-time in order, for example, to monitor service quality and recovery.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > 2.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Moreover, it may be an indication of how data-driven research models might be of relevance to tourism research.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > 2.
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Reviews may not always be subjective, therefore, the binary classification needs to be extended to a ternary classification that contains a third, 'objective' category.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Overview
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Data collection, data cleaning, mining process, and then evaluation and understanding of the results are the major steps used in most of the applications in relation to social media data analysis in tourism (Hippner & Rentzmann, 2006;Schmunk et al., 2014).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > 2.
What the sources say
- Not checked — no full text of the sourcecites Hippner & Rentzmann, 2006 source
- Not matchedcites Schmunk et al., 2014
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Sentiment analysis, in particular in relation to customer reviews, is built on the premise that information provided through text (e.g., a review) is either subjective (i.e.opinionated) or objective (i.e.factual).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Overview
What the sources say
- Not checked — no full text of the sourcecites Feldman, 2013 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Sentiment analysis comprises a multi-step process: a) data retrieval, b) data extraction and selection, c) data pre-processing, d) feature extraction, e) topic detection, and f) data mining process (e.g., Hippner & Rentzmann, 2006;Schmunk et al., 2014).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Sentiment analysis methods
What the sources say
- Not checked — no full text of the sourcecites Hippner & Rentzmann, 2006 source
- Not matchedcites Schmunk et al., 2014
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Relatively less research has focused on sentence level analysis, since it is more challenging to accurately extract polarity from a small number of words compared with paragraphs and documents (Brob, 2013;Choudhury, 2016;Höpken et al., 2016;Schmunk et al., 2014;Ribeiro et al., 2016).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Overview
What the sources say
- Not matchedcites Brob, 2013
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites Choudhury, 2016
- Not matchedcites Höpken et al., 2016
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
In the paper
“In addition, Naïve Bayes models were also adapted in an unsupervised fashion for sentiment analysis (e.g., Shimada et al., 2011).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Sentiment analysis methods
What the sources say
- Not checked — no full text of the sourcecites Shimada et al., 2011 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Hu and Liu (2004) later proposed a method to summarize customer reviews based on different product features.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Sentiment analysis methods
What the sources say
- Not checked — no full text of the sourcecites Hu and Liu (2004) source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Sentiment-aware nearest neighbour model (SANN) is a combination of dictionary-and learning-based approaches that initially classifies text as either a subjective or objective review (Pappas & Popescu-Belis, 2013).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Hybrid approach
What the sources say
- Not checked — no full text of the sourcecites Pappas & Popescu-Belis, 2013 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In hybrid approaches, dictionary and machine learning-based techniques can work in parallel to compute two sentiment polarities.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Hybrid approach
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Machine learning methods are further categorized into supervised and unsupervised approaches.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Sentiment analysis methods
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“Topic detection research dates back to 1998 where topic identification in the context of broadcast news was studied (Allan et al., 1998).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Sentiment analysis methods
What the sources say
- Not matchedcites Allan et al., 1998
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“One of the commonly used feature extraction techniques is based on term occurrences, called term frequency (TF) or term frequency-invers document frequency (TF-IDF).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Sentiment analysis methods
What the sources say
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Different methods were developed for dictionary-based approaches (Bjorkelund et al., 2012;Bucur, 2015;Garcia et al. 2012;Hutto & Gilbert, 2014;Levallois, 2013).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Figure 2. here
What the sources say
- Not matchedcites Bjorkelund et al., 2012
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites Bucur, 2015 source
- Not checked — no full text of the sourcecites Garcia et al. 2012
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“POS tagging is an important pre-processing task that generally forms a part of sentiment analysis by assigning each word a particular label (e.g., noun, verb, and adjective).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Sentiment analysis methods
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The dictionary-based approach also includes a subcategory called semantic-based approach (Tsytsarau & Palpanas, 2012).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Sentiment analysis methods
What the sources say
- Not checked — no full text of the sourcecites Tsytsarau & Palpanas, 2012 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“SentiWordNet in itself (Bucur, 2015;Garcia et al. 2012), and in combination with a simplified Lesk Algorithm, was also used in sentiment analysis (Bjorkelund et al., 2012).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Figure 2. here
What the sources say
- Not checked — no full text of the sourcecites Bucur, 2015 source
- Not checked — no full text of the sourcecites Garcia et al. 2012
- Not matchedcites Bjorkelund et al., 2012
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Each extracted review contains one or several sentences reflecting the reviewer's opinion.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Sentiment analysis methods
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Dictionary-based systems rely on the use of comprehensive sentiment lexicons and sets of fine-tuned rules.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Dictionary-based approach
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Moreover, creating a lexicon is a one-time effort and can be used forever and often across different domains.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Table 2 here
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This drawback may be mitigated by using a backward feedback from the current to the earlier steps.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Table 2 here
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Such trends can be particularly useful when visualised (e.g., on a map) to potential customers (Bjorkelund et al., 2012).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Table 2 here
What the sources say
- Not matchedcites Bjorkelund et al., 2012
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“Previous research established that features with high intensity over different time periods can be useful to detect abnormal changes in hotel reviews, and to analyse the reasons for these changes.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Table 2 here
What the sources say
- Not matchedcites Bjorkelund et al., 2012
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Related to the issue of bias, the review shows that the overall prediction performance of the methods can still be improved in both 2-class and 3-class sentiment analysis, but particularly in 3-class approaches.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Synthesis of the tourism-specific results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As these methods have provided reasonably good performances on Twitter data, and their lexicons also contain tourism related words and emoticons, they are applicable to the tourism domain.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Performance of sentiment analyses in tourism studies
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Due to the difficulty of detecting and finding implicit aspects in reviews, aspect-oriented sentiment analysis remains challenging.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Concluding remarks and future directions
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“One reason might be the existence of a larger number of positive texts and lexicons' bias towards positivity, as human language is inherently biased towards positivity (Dodds et al., 2015).”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Synthesis of the tourism-specific results
What the sources say
- Not checked — no full text of the sourcecites Dodds et al., 2015 source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Combining sentiment scores with other data, such as information on transport, weather, the environment, special events, crises, and other destination components may give rise to finding patterns that could not been seen and understood before.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Concluding remarks and future directions
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“To provide a negative evaluation of the luggage delivery process in the second example, common sense knowledge is required to interpret that an hour is not acceptable for luggage delivery.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Synthesis of the tourism-specific results
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In particular, the integration of several types of data has great potential for generating future insights at scales not seen before.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Concluding remarks and future directions
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Moreover, analysing the negation in reviews is semantically a complex task.”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Synthesis of the tourism-specific results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper relies on this without support.
Tourism content shared through social media has become a very influential information source that impacts tourism in many ways.
“Tourism content shared through social media has become a very influential information source that impacts tourism in many ways.”
The paper relies on this without support.
The volume of data on the Internet has reached a level that makes manual processing almost impossible, demanding new analytical approaches.
“However, the volume of data on the Internet has reached a level that makes manual processing almost impossible, demanding new analytical approaches.”
The paper relies on this without support.
In tourism, a service-based industry that relies on positive customer emotions and feedback, the concept of visitor satisfaction is of critical importance.
“In the context of tourism, a service-based industry that relies on positive customer emotions and feedback, the concept of visitor satisfaction is of critical importance.”
The paper relies on this without support.
Online social media, travel professional websites and platforms, and blogs present inexpensive means to gather rich, authentic, and unsolicited data on travellers' opinions.
“Importantly, online social media, travel professional websites and platforms, and blogs present inexpensive means to gather rich, authentic, and unsolicited data on travellers' opinions.”
The paper relies on this without support.
To analyse large data volumes more effectively, the demand for automatic multi-aspect algorithmic and machine-operated systems is increasing.
“To analyse large data volumes more effectively, the demand for automatic multi-aspect algorithmic and machine-operated systems is increasing.”
The paper relies on this without support.
Online data related to tourism activities are generated at such velocity that they outstrip the potential of traditional (paper and pen) surveys to capture events in real-time.
“Online data related to tourism activities are generated at such velocity that they outstrip the potential of traditional (paper and pen) surveys to capture events in real-time in order, for example, to monitor service quality and recovery.”
The paper relies on this without support.
Twitter's 140-character limit makes it suitable for mostly sentence-level sentiment analysis.
“Twitter, for instance, allows tweets of up to 140 characters in length, lending itself to a mostly sentence-level sentiment analysis.”
The paper relies on this without support.
Features derived based on a sentiment lexicon improve sentiment analysis results.
“Features derived based on a sentiment lexicon improve sentiment analysis results.”
The paper relies on this without support.
The use of a simple Bag of Words representation for sentiment analysis provides less favourable results compared to traditional topic classification, as semantic information needs also to be modelled by BoW.
“It has further been shown that the use of a simple Bag of Words (BoW) representation for sentiment analysis provides less favourable results compared to traditional topic classification, as in sentiment analysis, semantic information needs also to be modelled by BoW, which is a difficult task.”
The paper relies on this without support.
Considering higher order N-grams and complex linguistic features are helpful for polarity classification and can improve the results significantly.
“Considering higher order N-grams and complex linguistic features are helpful for polarity classification and can improve the results significantly.”
The paper relies on this without support.
The use of higher order N-grams and complex linguistic features is beneficial when large corpora are available for training the models.
“However, the use of higher order N-grams and complex linguistic features is beneficial when large corpora are available for training the models.”
The paper relies on this without support.
When using smaller corpora, a feature selection step is necessary to obtain satisfactory sentiment analysis results.
“When using smaller corpora, a feature selection step is necessary to obtain satisfactory sentiment analysis results.”
The paper relies on this without support.
Most travel agencies and hotel booking service providers employ scalar ratings to rate users' reviews, for example, scores between 1 and 5 stars.
“In relation to the kind of data used for sentiment analysis in tourism, we noted that most of the travel agencies and hotel booking service providers employ scalar ratings to rate users' reviews, for example, scores between 1 and 5 stars.”
The paper relies on this without support.
User-provided scores function as weakly labelled data, and can improve the classification accuracy, as well as help to verify polarity.
“However, from an analytical perspective, the user-provided scores function as weakly labelled data, and can improve the classification accuracy, as well as help to verify polarity.”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
“| Ganu et al. (2009) | Machine learning | Citysearch | English | Restaurant | Manual | 52,264 | 3,400 | 1,904 | 612 | 1,884 |”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Unigram
Reported 3400 · recomputed 4400
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
“| Kasper & Vela (2011 and 2012) Hybrid | Web, Blogs | German | Hotel | Manual | 4,792 | 4,792 | 2,240 | 1,183 | 938 | |”
§ Sentiment Analysis in Tourism: Capitalizing on Big Data Author > Unigram
Reported 4792 · recomputed 4361
Citations
Cites
Mining and summarizing customer reviews
Hu, M., & Liu, B. (2004) · Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1014052.1014073 · Cited by 7,793
Thumbs up?: sentiment classification using machine learning techniques
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Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
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VADER: a parsimonious rule-based model for sentiment analysis of social media text
Hutto, C., & Gilbert, E. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14550 · Cited by 6,087
Big Data, new epistemologies and paradigm shifts
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Social Media in Tourism and Hospitality: A Literature Review
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Techniques and applications for sentiment analysis
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Use and Impact of Online Travel Reviews
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- Chua A. Y. K., Banerjee S. 2013. “Reliability of Reviews on the Internet: The Case of TripAdvisor.” In Proceedings of the World Congress on Engineering and Computer Science, 453-57. New York: Springer.
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Mariani, M. M., Baggio, R., Fuchs, M., & Höpken, W. (2018) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-07-2017-0461 · Cited by 502
Applications of big data in emerging management disciplines: A literature review using text mining
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Kokab, S. T., Asghar, S., & Naz, S. (2022) · Array · doi:10.1016/j.array.2022.100157 · Cited by 297
Tourism recommendation system based on semantic clustering and sentiment analysis
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Mathayomchan, B., & Taecharungroj, V. (2020) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2020.102641 · Cited by 151
An online reviews-driven method for the prioritization of improvements in hotel services
Zhang, C., Xu, Z., Gou, X., & Chen, S. (2021) · Tourism Management · doi:10.1016/j.tourman.2021.104382 · Cited by 162
Understanding service attributes of robot hotels: A sentiment analysis of customer online reviews
Luo, J. M., Vu, H. Q., Li, G., & Law, R. (2021) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2021.103032 · Cited by 162
Mehraliyev, F., Kirilenko, A., & Choi, Y. (2020) · Tourism Management · doi:10.1016/j.tourman.2020.104096 · Cited by 136
Hu, F., Teichert, T., Deng, S., Yong, L., & Zhou, G. (2021) · Tourism Management · doi:10.1016/j.tourman.2021.104320 · Cited by 133
The impact of online reputation on hotel profitability
Anagnostopoulou, S. C., Buhalis, D., Kountouri, I., Manousakis, E. G., & Tsekrekos, A. Ε. (2019) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-03-2019-0247 · Cited by 151
Obiedat, R., Qaddoura, R., Al-Zoubi, A. M., Al-Qaisi, L., Harfoushi, O., Alrefai, M., & Faris, H. (2022) · IEEE Access · doi:10.1109/access.2022.3149482 · Cited by 187
“As a result, ML is a preferable alternative since it includes highly illustrative and flexible models, as stated by Alaei et al. [31].”
MethodGaur, L., Afaq, A., Solanki, A., Singh, G., Sharma, S., Jhanjhi, N. Z., My, H. T., & Le, D. (2021) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2021.107374 · Cited by 120
“Positive customer feedback and emotions are critical for a service-based industry like tourism [11].”
BackgroundMethodTopic Modeling and Sentiment Analysis of Online Review for Airlines
Kwon, H.-J., Ban, H.-J., Jun, J., & Kim, H. (2021) · Information · doi:10.3390/info12020078 · Cited by 122
Li, H., Ji, H., Liu, H., Cai, D., & Gao, H. (2022) · Tourism Management · doi:10.1016/j.tourman.2022.104559 · Cited by 126
Mariani, M. M., & Borghi, M. (2023) · International Journal of Production Research · doi:10.1080/00207543.2022.2160027 · Cited by 157
“As far as the focal independent variables are concerned, we created them using extant theorisation revolving around the application of Big Data analytics techniques to online review data (Alaei, Becken, and Stantic 2019; Bi et al. 2019; Mariani and Baggio 2021).”
MethodUnderstanding sentiment of national park visitors from social media data
Hausmann, A., Toivonen, T., Fink, C., Heikinheimo, V., Kulkarni, R., Tenkanen, H., & Minin, E. D. (2020) · People and Nature · doi:10.1002/pan3.10130 · Cited by 106
Using deep learning and visual analytics to explore hotel reviews and responses
Chang, Y., Ku, C., & Chen, C. (2020) · Tourism Management · doi:10.1016/j.tourman.2020.104129 · Cited by 105
Sentiment analysis – a review and agenda for future research in hospitality contexts
Ma, E., Cheng, M., & Hsiao, A. (2018) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-10-2017-0704 · Cited by 103
Mirzaalian, F., & Halpenny, E. A. (2021) · Journal of Destination Marketing & Management · doi:10.1016/j.jdmm.2021.100598 · Cited by 106
Chen, J., Becken, S., & Stantić, B. (2022) · Annals of Tourism Research · doi:10.1016/j.annals.2022.103371 · Cited by 100
Self-citationRevisiting Tourism Destination Image: A Holistic Measurement Framework Using Big Data
Bui, V., Alaei, A., Vu, H. Q., Li, G., & Law, R. (2021) · Journal of Travel Research · doi:10.1177/00472875211024749 · Cited by 115
Self-citationArtificial intelligence in tourism: insights and future research agenda
Tuo, Y., Wu, J., Zhao, J., & Si, X. (2024) · Tourism Review · doi:10.1108/tr-03-2024-0180 · Cited by 125
Chang, Y., Ku, C., & Nguyen, D.-D. L. (2021) · Information & Management · doi:10.1016/j.im.2021.103587 · Cited by 98
Multi-Level Fine-Scaled Sentiment Sensing with Ambivalence Handling
Wang, Z., Ho, S.-B., & Cambria, E. (2020) · International Journal of Uncertainty Fuzziness and Knowledge-Based Systems · doi:10.1142/s0218488520500294 · Cited by 90
Artificial intelligence in hospitality and tourism. State of the art and future research avenues
Nannelli, M., Capone, F., & Lazzeretti, L. (2023) · European Planning Studies · doi:10.1080/09654313.2023.2180321 · Cited by 109
“Studies con fi rm that using BD as a methodology can improve the accuracy of estimates compared to other methods such as questionnaires and interviews, which can also be more costly in terms of time and money (Alaei, Becken, and Stantic 2019).”
MethodThe role of emotions in the consumer meaning-making of interactions with social robots
Borghi, M., & Mariani, M. M. (2022) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2022.121844 · Cited by 89
Liu, J., Yu, Y., Mehraliyev, F., et al. (2022) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-06-2021-0749 · Cited by 89
Differences between TripAdvisor and Booking.com in branding co-creation
Tiago, M. T. B., Arruda, C., Tiago, F., et al. (2020) · Journal of Business Research · doi:10.1016/j.jbusres.2020.09.050 · Cited by 88
Sentiment analysis of online destination image of Hong Kong held by mainland Chinese tourists
Jiang, Q., Chan, C., Eichelberger, S., Ma, H., & Pikkemaat, B. (2021) · Current Issues in Tourism · doi:10.1080/13683500.2021.1874312 · Cited by 101
“…to take advantage of big data analysis, which helps inform and formulate holistic tourism planning since small-scale surveys and interviews might not be sufficient to explain the ever-changing trends in the tourism industry (Alaei et al., 2019; Fuchs et al., 2014; Li et al., 2018; Lian & Yu, 2017).”
BackgroundDeep Learning-based Sentiment Analysis and Topic Modeling on Tourism During Covid-19 Pandemic
Mishra, R. K., Urolagin, S., Jothi, J. A. A., Neogi, A. S., & Nawaz, N. (2021) · Frontiers in Computer Science · doi:10.3389/fcomp.2021.775368 · Cited by 84
“A study (Alaei et al., 2017) has employed various sentiment analysis techniques in tourism, which are examined and evaluated in terms of datasets used and performance on key assessment measures.”
BackgroundEnvironmental discourse in hotel online reviews: a big data analysis
Mariani, M. M., & Borghi, M. (2020) · Journal of Sustainable Tourism · doi:10.1080/09669582.2020.1858303 · Cited by 91
“JOURNAL OF SUSTAINABLE TOURISM 835 measure has been deployed because recent research (Alaei et al., 2019) has found that it outperforms other sentiment analysis classifiers used in the tourism and hospitality domain, while being consistent with them.”
MethodInfluentialVatambeti, R., Mantena, S. V., Kiran, K. V. D., et al. (2023) · Cluster Computing · doi:10.1007/s10586-023-03970-7 · Cited by 81
“Alaei et al. [17] conducted a study that compares and contrasts several sentiment analysis methods used in the tourism industry in terms of datasets used and results on key assessment criteria.”
A Structural Topic Modeling-Based Bibliometric Study of Sentiment Analysis Literature
Chen, X., & Xie, H. (2020) · Cognitive Computation · doi:10.1007/s12559-020-09745-1 · Cited by 81
Social media analytics in hospitality and tourism
Mirzaalian, F., & Halpenny, E. A. (2019) · Journal of Hospitality and Tourism Technology · doi:10.1108/jhtt-08-2018-0078 · Cited by 79
Social Media Toxicity Classification Using Deep Learning: Real-World Application UK Brexit
Fan, H. M., Du, W., Dahou, A., Ewees, A. A., Yousri, D., Elaziz, M. A., Elsheikh, A. H., Abualigah, L., & Al‐qaness, M. A. A. (2021) · Electronics · doi:10.3390/electronics10111332 · Cited by 82
“…[5], domestic violence detection [6,7], clustering analysis [8,9], extremist affiliations [10], prediction and analysis of elections [11–13], analysis of happiness patterns [14], education evaluation [15], football events [16], multimedia summarization [17,18], and many other applications [19–22].”
BackgroundSerrano, L., Ariza‐Montes, A., Nader, M., Sianes, A., & Law, R. (2020) · Journal of Sustainable Tourism · doi:10.1080/09669582.2020.1838529 · Cited by 90
“Online comments posted by tourists on social networks generate millions of data points that, when examined through the lens of sentiment analysis, allow us to understand better their perceptions and satisfaction and that of other tourism industry players (Alaei et al., 2019).”
BackgroundOrea-Giner, A., Moraleda, L. F., Villacé-Molinero, T., Mazón, A. I. M., & Calero-Sanz, J. (2022) · Tourism Management · doi:10.1016/j.tourman.2022.104586 · Cited by 75
Agrawal, R., Wankhede, V. A., Kumar, A., V, Luthra, S., & Huisingh, D. (2022) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2022.100122 · Cited by 76
Are environmental-related online reviews more helpful? A big data analytics approach
Mariani, M. M., & Borghi, M. (2021) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-06-2020-0548 · Cited by 78
Yuan, Y., Chan, C., Eichelberger, S., Ma, H., & Pikkemaat, B. (2022) · Journal of Tourism Futures · doi:10.1108/jtf-04-2021-0094 · Cited by 75
Which factors influence locals’ and visitors’ overall restaurant evaluations?
Pezenka, I., & Weismayer, C. (2020) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-09-2019-0796 · Cited by 73
Multi-Source Domain Adaptation for Visual Sentiment Classification
Lin, C., Zhao, S., Meng, L., & Chua, T. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i03.5651 · Cited by 72
“It offers a great opportunity for visual sentiment analysis that infers their emotional behaviors and provide personalized services (Zhao et al. 2018b), such as blog recommendation (Borth et al. 2013) and tourism (Alaei, Becken, and Stantic 2019).”
BackgroundConvLSTMConv network: a deep learning approach for sentiment analysis in cloud computing
Ghorbani, M., Bahaghighat, M., Xin, Q., et al. (2020) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-020-00162-1 · Cited by 73
“In [17], the authors applied sentiment analysis on the topic of tourism.”
BackgroundPaolanti, M., Mancini, A., Frontoni, E., Felicetti, A., Marinelli, L., Marcheggiani, E., & Pierdicca, R. (2021) · Information Technology & Tourism · doi:10.1007/s40558-021-00196-4 · Cited by 87
Determinants of Guest Experience in Airbnb: A Topic Modeling Approach Using LDA
Sutherland, I., & Kiatkawsin, K. (2020) · Sustainability · doi:10.3390/su12083402 · Cited by 82
“Of the new methodologies in text analytics, sentiment analysis alone has limited effectiveness for gaining insight from large datasets of aggregated reviews, despite being common in the hospitality literature [7,8].”
MethodKim, T.-K., Jo, H., Yhee, Y., & Koo, C. (2022) · Electronic Markets · doi:10.1007/s12525-021-00514-y · Cited by 83
“Alaei et al. (2019) compared different sentiment analysis methods in the context of hospitality research.”
MethodMariani, M. M., & Borghi, M. (2022) · Journal of Sustainable Tourism · doi:10.1080/09669582.2022.2033982 · Cited by 84
“For instance, tourism and hospitality scholars have deployed techniques such as sentiment analysis and analytics (e.g. Alaei et al., 2019; Hao et al., 2020; Mehraliyev et al., 2020; Serrano et al., 2021), textual meta-data processing (e.g. Miah et al., 2017), machine learning techniques (e.g. Ahani…”
BackgroundMethodTwitter sentiment analysis of app based online food delivery companies
Trivedi, S. K., & Singh, A. (2021) · Global Knowledge, Memory and Communication · doi:10.1108/gkmc-04-2020-0056 · Cited by 68
Álvarez‐Carmona, M. Á., Aranda, R., Rodríguez‐González, A. Y., Fajardo‐Delgado, D., Sánchez, M. G., Pérez-Espinosa, H., Martı́nez-Miranda, J., Rodríguez, R. G., Bustio-Martínez, L., & Pacheco, A. D. (2022) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2022.10.010 · Cited by 81
Artificial Intelligence Tools for Smart Tourism Development
Gajdošík, T., & Marciš, M. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-19810-7_39 · Cited by 67
Listen to social media users: Mining Chinese public perception of automated vehicles after crashes
Jing, P., Cai, Y., Wang, B., Wang, B., Huang, J.-H., Jiang, C., & Yang, C. (2023) · Transportation Research Part F Traffic Psychology and Behaviour · doi:10.1016/j.trf.2023.01.018 · Cited by 70
Parimala, M., Ramu, S. P., Maddikunta, P. K. R., Chowdhary, C. L., Poluru, R. K., & Khan, S. A. (2020) · Software Practice and Experience · doi:10.1002/spe.2851 · Cited by 67
“In Reference 28, the authors examine how Flickr data can be used to assess the disaster situation.”
BackgroundInfluentialIntegrating technology to service innovation
Lee, M., Ahn, J., Shin, M., Kwon, W., & Back, K. (2019) · Journal of Hospitality and Tourism Technology · doi:10.1108/jhtt-01-2019-0013 · Cited by 75
Chaturvedi, R., Verma, S., Ali, F., & Kumar, S. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2238353 · Cited by 67
“For example, Alaei et al. (2019) examined sentiment analysis in tourism to garner latent insights into online reviews.”
Designing tourist experiences amidst air pollution: A spatial analytical approach using social media
Zhang, X., Yang, Y., Zhang, Y. L., & Zhang, Z. (2020) · Annals of Tourism Research · doi:10.1016/j.annals.2020.102999 · Cited by 65
Sustainable, Smart and Muslim-Friendly Tourist Destinations
Valiño, P. C., Bolifa, F., & Núñez‐Barriopedro, E. (2020) · Sustainability · doi:10.3390/su12051778 · Cited by 65
Exploring the multi-dimensionality of authenticity in dining experiences using online reviews
Le, T. H., Arcodia, C., Novais, M. A., Kralj, A., & Phan, T. C. (2021) · Tourism Management · doi:10.1016/j.tourman.2021.104292 · Cited by 71
A fine-grained sentiment analysis of online guest reviews of economy hotels in China
Luo, J., Huang, S. S., & Wang, R. (2020) · Journal of Hospitality Marketing & Management · doi:10.1080/19368623.2020.1772163 · Cited by 65
López-Belmonte, J., Robles, A. S., Moreno-Guerrero, A.-J., & González, M. E. P. (2020) · Symmetry · doi:10.3390/sym12040495 · Cited by 82
“Machine learning has been used in Big Data, and in numerous sets of fields [15–17].”
MethodDeep learning in hospitality and tourism: a research framework agenda for future research
Essien, A. E., & Chukwukelu, G. (2022) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-09-2021-1176 · Cited by 73
Research on Sentiment Classification of Online Travel Review Text
Chen, W., Xu, Z., Zheng, X., Yu, Q., & Luo, Y. (2020) · Applied Sciences · doi:10.3390/app10155275 · Cited by 69
Reading between the lines: analyzing online reviews by using a multi-method Web-analytics approach
Gour, A., Aggarwal, S., & Erdem, M. (2021) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-07-2020-0760 · Cited by 62
Twitter public sentiment dynamics on cruise tourism during the COVID-19 pandemic
Lu, Y., & Zheng, Q. (2020) · Current Issues in Tourism · doi:10.1080/13683500.2020.1843607 · Cited by 61
“Most recently, Alaei et al. (2019) provide a comprehensive review of sentiment analysis in tourism and recommend that it should become an important tool in tourism research.”
BackgroundForecasting daily attraction demand using big data from search engines and social media
Tian, F., Yang, Y., Mao, Z., & Tang, W. (2021) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-06-2020-0631 · Cited by 65
Machine Learning in Detecting COVID-19 Misinformation on Twitter
Alenezi, M. N., & AlQenaei, Z. M. (2021) · Future Internet · doi:10.3390/fi13100244 · Cited by 59
Digital Tourism and Smart Development: State-of-the-Art Review
Wu, W., Xu, C., Zhao, M., Li, X., & Law, R. (2024) · Sustainability · doi:10.3390/su162310382 · Cited by 98
Vorobeva, D., Pinto, D. C., António, N., & Mattila, A. S. (2023) · Current Issues in Tourism · doi:10.1080/13683500.2023.2214353 · Cited by 68
“Social media content related to tourism and hospitality has become a very powerful source of information (Alaei et al., 2019).”
BackgroundSocial Media Data-Based Sentiment Analysis of Tourists’ Air Quality Perceptions
Tao, Y., Zhang, F., Shi, C., & Chen, Y. (2019) · Sustainability · doi:10.3390/su11185070 · Cited by 57
“China is both a major tourist destination and a social media superpower; tourists have been very keen on sharing a wide range of tourism content on social media [11].”
BackgroundMethodResultInfluentialRecurrent Neural Network (RNN) to Analyse Mental Behaviour in Social Media
Bouarara, H. A. (2021) · International Journal of Software Science and Computational Intelligence · doi:10.4018/ijssci.2021070101 · Cited by 58
Zheng, T., Wu, F., Law, R., Qiu, Q., & Wu, R. (2020) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2020.102658 · Cited by 59
Oliveira, J. S., Ifie, K., Sykora, M. D., Tsougkou, E., Castro, V., & Elayan, S. (2021) · Journal of Business Research · doi:10.1016/j.jbusres.2021.11.063 · Cited by 56
A unified position-aware convolutional neural network for aspect based sentiment analysis
Wang, X., Li, F., Zhang, Z., Xu, G., Zhang, J., & Sun, X. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.03.092 · Cited by 62
Saoualih, A., Safaa, L., Bouhatous, A., et al. (2024) · Sustainability · doi:10.3390/su16156378 · Cited by 53
Unsupervised Semantic Approach of Aspect-Based Sentiment Analysis for Large-Scale User Reviews
Al-Ghuribi, S. M., Noah, S. A. M., & Tiun, S. B. (2020) · IEEE Access · doi:10.1109/access.2020.3042312 · Cited by 53
“Additionally, it has been used in many systems for different purposes such as tourism [16], online education [17], and transportation [18].”
BackgroundThe embedded feelings in local gastronomy: a sentiment analysis of online reviews
Yu, J., & Zhang, X. (2020) · Journal of Hospitality and Tourism Technology · doi:10.1108/jhtt-02-2019-0028 · Cited by 56
Hidden theorizing in big data analytics: With a reference to tourism design research
Mazanec, J. A. (2020) · Annals of Tourism Research · doi:10.1016/j.annals.2020.102931 · Cited by 55
Borghi, M., & Mariani, M. M. (2023) · Journal of Travel Research · doi:10.1177/00472875231190610 · Cited by 60
“To operationalize the aforementioned measures, following an automated process, we leveraged recent progress in the literature encompassing big data analytics and eWOM (Alaei et al., 2019; Bi et al., 2019; M. Mariani & Wirtz, 2023).”
MethodInfluentialConsumer engagement: the role of social currency in online reviews
Kesgin, M., & Murthy, R. S. (2018) · Service Industries Journal · doi:10.1080/02642069.2018.1553237 · Cited by 59
“For a complete review of sentiment analysis see Alaei et al. (2017).”
BackgroundSimilarity and Consistency in Hotel Online Ratings across Platforms
Alcañiz, J. E. B., William, E., & Soria‐Olivas, E. (2019) · Journal of Travel Research · doi:10.1177/0047287519859705 · Cited by 55
Fu, Y., Hao, J., Li, X. R., & Hsu, C. H. C. (2018) · Journal of Travel Research · doi:10.1177/0047287518772361 · Cited by 50
Text Analysis in Tourism and Hospitality: A Comprehensive Review
Bi, J.-W., Zhu, X.-E., & Han, T.-Y. (2024) · Journal of Travel Research · doi:10.1177/00472875241247318 · Cited by 64
“…to text analysis in this field exist, they mainly focus on specific types of text data, such as online reviews (Kwok et al., 2017) and social media data (Nusair, 2020), or specific text analysis methods, such as sentiment analysis (Alaei et al., 2019; Ma et al., 2018; Mehraliyev et al., 2022 ).”
Sentiment Analysis for e-Payment Service Providers Using Evolutionary eXtreme Gradient Boosting
Qudah, D. A., Al-Zoubi, A. M., Castillo, P. Á., & Faris, H. (2020) · IEEE Access · doi:10.1109/access.2020.3032216 · Cited by 52
“For this reason, sentiment analysis — a new method that extracts opinions on a specific topic or service and then analyzes them — has emerged [5]–[8].”
MethodTo, W. M., & Yu, B. T. W. (2025) · Tourism and Hospitality · doi:10.3390/tourhosp6020063 · Cited by 74
“The analysis indicated that AI has become a crucial tool for strategic decision-making, risk assessment, consumer segmentation, service quality enhancement, and overall service design improvement (Tussyadiah, 2020; Alaei et al., 2019).”
Optimizing service offerings using asymmetric impact-sentiment-performance analysis
Hu, F., Li, H., Yong, L., & Teichert, T. (2020) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2020.102557 · Cited by 49
Social media, media and urban transformation in the context of overtourism
Jang, H., & Park, M. (2020) · International Journal of Tourism Cities · doi:10.1108/ijtc-08-2019-0145 · Cited by 50
Learn#: A Novel incremental learning method for text classification
Shan, G., Xu, S., Li, Y., Jia, S., & Xiang, Y. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113198 · Cited by 49
Dark tourism spectrum: Visual expression of dark experience
Lv, X., Luo, H., Xu, S., Sun, J., Lu, R., & Hu, Y. (2022) · Tourism Management · doi:10.1016/j.tourman.2022.104580 · Cited by 55
Testing loss aversion and diminishing sensitivity in review sentiment
Sharma, A., Park, S., & Nicolau, J. L. (2019) · Tourism Management · doi:10.1016/j.tourman.2019.104020 · Cited by 46
“A sentiment analysis as part of opinon mining was applied to uncover opinions and to assess contextual polarity of online consumers within a given text (Alaei, Becken and Stantic, 2019).”
Sentiment mining in a collaborative learning environment: capitalising on big data
Jena, R. K. (2019) · Behaviour and Information Technology · doi:10.1080/0144929x.2019.1625440 · Cited by 45
“…and N-grams), linguistic features (e.g. lemmatization and syntax) and sentiment shifter features (e.g. intensi fi cation, neutralization and negation) can also be considered on educational data to study applicability and performance of di ff erent SM algorithms (Alaei, Becken, and Stantic 2017).”
BackgroundDiscovering a tourism destination with social media data: BERT-based sentiment analysis
Viñán-Ludeña, M. S., & de Campos, L. M. (2022) · Journal of Hospitality and Tourism Technology · doi:10.1108/jhtt-09-2021-0259 · Cited by 55
Kiatkawsin, K., Sutherland, I., & Kim, J. (2020) · Sustainability · doi:10.3390/su12166673 · Cited by 44
“In hospitality and tourism studies, sentiment analysis has been the most popular application in automated text analysis [8,9,12].”
Method
Metadata
- Authors
- Alaei, A. R., Becken, S., & Stantic, B.
- Affiliations
- School of Information and Communication Technology, Griffith University, Australia
- Griffith University (AU)
- ORCID
- Becken, Susanne
- Venue
- Journal of Travel Research
- Publisher
- SAGE Publications
- Published
- 2017-12-14
- Volume
- 58
- Issue
- 2
- Pages
- 175-191
- ISSN
- 0047-2875 (ISSN-L)
- 1552-6763
- OpenAlex
- W2774008574
- Semantic Scholar
- 5bc511aa30f72720260d792e57537379fb04c395
- Cited by
- 622
- References
- 75
- Citations by year
- 2026: 52 · 2025: 67 · 2024: 85 · 2023: 88 · 2022: 105 · 2021: 92 · 2020: 85 · 2019: 38 · 2018: 10
- FWCI
- 21.71
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- Journal of Travel Research (publishedVersion)
- Griffith Research Online (submittedVersion)
- Cross Connect (Southern Cross University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › Business, Management and Accounting › Tourism, Leisure and Hospitality Management › Diverse Aspects of Tourism Research
- Social Sciences › Business, Management and Accounting › Marketing › Digital Marketing and Social Media
- Keywords
- sentiment analysis, tourism, big data, social media, online reviews, tourist experience, computational text analysis, evaluation metrics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-11, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-11 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
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