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Sentiment strength detection for the social web
Abstract
Sentiment analysis is concerned with the automatic extraction of sentiment‐related information from text. Although most sentiment analysis addresses commercial tasks, such as extracting opinions from product reviews, there is increasing interest in the affective dimension of the social web, and Twitter in particular. Most sentiment analysis algorithms are not ideally suited to this task because they exploit indirect indicators of sentiment that can reflect genre or topic instead. Hence, such algorithms used to process social web texts can identify spurious sentiment patterns caused by topics rather than affective phenomena. This article assesses an improved version of the algorithm SentiStrength for sentiment strength detection across the social web that primarily uses direct indications of sentiment. The results from six diverse social web data sets (MySpace, Twitter, YouTube, Digg, Runners World, BBC Forums) indicate that SentiStrength 2 is successful in the sense of performing better than a baseline approach for all data sets in both supervised and unsupervised cases. SentiStrength is not always better than machine‐learning approaches that exploit indirect indicators of sentiment, however, and is particularly weaker for positive sentiment in news‐related discussions. Overall, the results suggest that, even unsupervised, SentiStrength is robust enough to be applied to a wide variety of different social web contexts.
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From Safety of creatine supplementation: analysis of the prevalence of reported side effects in clinical trials and adverse event reports (2025) · cites this paper as [69]
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Sorted by citations
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Metadata
- Authors
- Thelwall, M., Buckley, K., & Paltoglou, G.
- Affiliations
- University of Wolverhampton (GB)
- ORCID
- Thelwall, Mike
- Venue
- Journal of the American Society for Information Science and Technology
- Publisher
- Wiley
- Published
- 2011-10-13
- Volume
- 63
- Issue
- 1
- Pages
- 163-173
- ISSN
- 1532-2882 (ISSN-L)
- 1532-2890
- OpenAlex
- W1987425720
- Cited by
- 1,062
- References
- 65
- Citations by year
- 2026: 18 · 2025: 40 · 2024: 35 · 2023: 60 · 2022: 64 · 2021: 90 · 2020: 99 · 2019: 94 · 2018: 123 · 2017: 108 · 2016: 101 · 2015: 96 · 2014: 64 · 2013: 50 · 2012: 19
- FWCI
- 39.19
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the American Society for Information Science and Technology (publishedVersion)
- Funders
- Seventh Framework Programme
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Keywords
- SentiStrength, Web 2.0, Twitter, unsupervised sentiment analysis, online forums, user-generated content
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