An Improved Sentiment Analysis Approach to Detect Radical Content on Twitter

kamel Ahsene Djaballah, Kamel Boukhalfa, Omar Boussaid and Yassine Ramdane
Additional contact information
kamel Ahsene Djaballah: University of Science and Technology Houari Boumediene, Algiers, Algeria
Kamel Boukhalfa: University of Science and Technology Houari Boumediene, Algiers, Algeria
Omar Boussaid: ERIC Laboratory EA 3083, University of Lyon 2, Lyon, France
Yassine Ramdane: ERIC Laboratory EA 3083, University of Lyon 2, Lyon, France

International Journal of Information Technology and Web Engineering (IJITWE), 2021, vol. 16, issue 4, 52-73

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.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 18/IJITWE.2021100103 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jitwe0:v:16:y:2021:i:4:p:52-73

Access Statistics for this article

International Journal of Information Technology and Web Engineering (IJITWE) is currently edited by Ghazi I. Alkhatib

More articles in International Journal of Information Technology and Web Engineering (IJITWE) from IGI Global
Bibliographic data for series maintained by Journal Editor ().