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An Improved Sentiment Analysis Approach to Detect Radical Content on Twitter

An Improved Sentiment Analysis Approach to Detect Radical Content on Twitter

kamel Ahsene Djaballah, Kamel Boukhalfa, Omar Boussaid, Yassine Ramdane
Copyright: © 2021 |Volume: 16 |Issue: 4 |Pages: 22
ISSN: 1554-1045|EISSN: 1554-1053|EISBN13: 9781799859772|DOI: 10.4018/IJITWE.2021100103
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MLA

Djaballah, kamel Ahsene, et al. "An Improved Sentiment Analysis Approach to Detect Radical Content on Twitter." IJITWE vol.16, no.4 2021: pp.52-73. http://doi.org/10.4018/IJITWE.2021100103

APA

Djaballah, K. A., Boukhalfa, K., Boussaid, O., & Ramdane, Y. (2021). An Improved Sentiment Analysis Approach to Detect Radical Content on Twitter. International Journal of Information Technology and Web Engineering (IJITWE), 16(4), 52-73. http://doi.org/10.4018/IJITWE.2021100103

Chicago

Djaballah, kamel Ahsene, et al. "An Improved Sentiment Analysis Approach to Detect Radical Content on Twitter," International Journal of Information Technology and Web Engineering (IJITWE) 16, no.4: 52-73. http://doi.org/10.4018/IJITWE.2021100103

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Abstract

Social networks are used by terrorist groups and people who support them to propagate their ideas, ideologies, or doctrines and share their views on terrorism. To analyze tweets related to terrorism, several studies have been proposed in the literature. Some works rely on data mining algorithms; others use lexicon-based or machine learning sentiment analysis. Some recent works adopt other methods that combine multi-techniques. This paper proposes an improved approach for sentiment analysis of radical content related to terrorist activity on Twitter. Unlike other solutions, the proposed approach focuses on using a dictionary of weighted terms, the Word2vec method, and trigrams, with a classification based on fuzzy logic. The authors have conducted experiments with 600 manually annotated tweets and 200,000 automatically collected tweets in English and Arabic to evaluate this approach. The experimental results revealed that the new technique provides between 75% to 78% of precision for radicality detection and 61% to 64% to detect radicality degrees.

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