etBlogAnalysis —Mining Virtual Communities using Statistical and Linguistic Methods for Quality Control in Tourism
Integrity signals
Registry
Cites
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
It is possible to design a sentiment analysis model by incorporating both dictionary and machine learning based methods at different stages of the model.
“It is also possible to design a sentiment analysis model by incorporating both dictionary and machine learning based methods at different stages of the model (Waldhör et al., 2008;Claster et al., 2010a;Claster et al., 2010b;Kasper & Vela, 2011;Claster et al., 2013;Pappas & Popescu-Belis, 2013;Schmunk et al., 2014;Chiu et al., 2015).”
From Sentiment Analysis in Tourism: Capitalizing on Big Data (2019) · cites this paper as Waldhör et al., 2008
Waldhör and Rind (2008) proposed combining a linguistic parsing methodology with information and terminology extraction methods to determine sentiment polarity of online blog reviews.
“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.”
From Sentiment Analysis in Tourism: Capitalizing on Big Data (2019) · cites this paper as Waldhör and Rind, 2008
The citation lists haven’t been fetched yet
References 18 · Cited by 14
- Authors
- Waldhör, K., & Rind, A.
- Venue
- Information and Communication Technologies in Tourism 2008
- Publisher
- Springer Vienna
- Published
- 2008-02-15
- Pages
- 453-462
- OpenAlex
- W1931045278
- Cited by
- 14
- References
- 18
- Citations by year
- 2025: 1 · 2021: 1 · 2019: 1 · 2018: 1 · 2017: 3 · 2016: 1 · 2015: 1 · 2012: 2
- FWCI
- 1.68
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Springer eBooks (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › Sociology and Political Science › Diverse Aspects of Tourism Research
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Keywords
- online communities, blogs, online travel reviews, sentiment extraction, text classification, web crawling, term extraction, polarity analysis, tourism promotion
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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