Techniques and applications for sentiment analysis
Abstract
The main applications and challenges of one of the hottest research areas in computer science.
Integrity signals
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Sentiment analysis, in particular in relation to customer reviews, is built on the premise that information provided through text is either subjective or objective.
“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).”
From Sentiment Analysis in Tourism: Capitalizing on Big Data (2019) · cites this paper as Feldman, 2013
Sentiment analysis in tourism is only beginning to gain in popularity.
“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).”
From Sentiment Analysis in Tourism: Capitalizing on Big Data (2019) · cites this paper as Feldman, 2013
In the data mining process, different types of sentiment analysis methods can be distinguished: machine learning, rule/dictionary-based and hybrid approaches.
“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).”
From Sentiment Analysis in Tourism: Capitalizing on Big Data (2019) · cites this paper as Feldman, 2013
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Addressing age-related bias in sentiment analysis,
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Using rhetorical structure in sentiment analysis
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- Authors
- Feldman, R.
- Affiliations
- Hebrew University of Jerusalem (IL)
- Corresponding author
- Feldman, Ronen
- Venue
- Communications of the ACM
- Published
- 2013-04
- Volume
- 56
- Issue
- 4
- Pages
- 82-89
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W2031998113
- Cited by
- 1,489
- References
- 0
- Citations by year
- 2026: 49 · 2025: 59 · 2024: 97 · 2023: 139 · 2022: 182 · 2021: 166 · 2020: 122 · 2019: 114 · 2018: 121 · 2017: 122 · 2016: 115 · 2015: 101 · 2014: 81 · 2013: 20 · 2012: 1
- FWCI
- 116.87
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Communications of the ACM (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- sentiment analysis, text classification, natural language processing applications
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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.
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