Text Mining
Integrity signals
Registry
Cites
Statistics
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?
Data collection, data cleaning, mining process, and then evaluation and understanding of the results are the major steps used in most applications in relation to social media data analysis in tourism.
“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).”
From Sentiment Analysis in Tourism: Capitalizing on Big Data (2019) · cites this paper as Hippner & Rentzmann, 2006
Sentiment analysis comprises a multi-step process: data retrieval, data extraction and selection, data pre-processing, feature extraction, topic detection, and data mining process.
“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).”
From Sentiment Analysis in Tourism: Capitalizing on Big Data (2019) · cites this paper as Hippner & Rentzmann, 2006
One commonly used feature extraction technique is based on term occurrences, called term frequency (TF) or term frequency-invers document frequency (TF-IDF).
“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).”
From Sentiment Analysis in Tourism: Capitalizing on Big Data (2019) · cites this paper as Hippner & Rentzmann, 2006
Cited by
Sorted by citations
Sentiment Analysis in Tourism: Capitalizing on Big Data
Alaei, A. R., Becken, S., & Stantic, B. (2019) · Journal of Travel Research · doi:10.1177/0047287517747753 · Cited by 622 · see citing works
- Authors
- Hippner, H., & Rentzmann, R.
- Affiliations
- Catholic University of Eichstätt-Ingolstadt (DE)
- Corresponding author
- Hippner, Hajo
- Venue
- Informatik-Spektrum
- Published
- 2006-06-20
- Volume
- 29
- Issue
- 4
- Pages
- 287-290
- ISSN
- 0170-6012 (ISSN-L)
- 1432-122X
- OpenAlex
- W4214638364
- Cited by
- 36
- References
- 0
- Citations by year
- 2025: 2 · 2024: 3 · 2023: 3 · 2022: 5 · 2021: 2 · 2020: 3 · 2019: 5 · 2018: 3 · 2017: 1 · 2014: 3 · 2013: 1 · 2012: 1
- FWCI
- 1.52
- Type
- journal article
- Language
- de
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Informatik-Spektrum (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- text mining, natural language processing, term extraction, data preparation, text preprocessing
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.
What counts as a claim ›How we measure confidence ›How we check this ›
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