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A novel adaptable approach for sentiment analysis on big social data

Abstract : Gathering public opinion by analyzing big social data has attracted wide attention due to its interactive and real time nature. For this, recent studies have relied on both social media and sentiment analysis in order to accompany big events by tracking people’s behavior. In this paper, we propose an adaptable sentiment analysis approach that analyzes social media posts and extracts user’s opinion in real-time. The proposed approach consists of first constructing a dynamic dictionary of words’ polarity based on a selected set of hashtags related to a given topic, then, classifying the tweets under several classes by introducing new features that strongly fine-tune the polarity degree of a post. To validate our approach, we classified the tweets related to the 2016 US election. The results of prototype tests have performed a good accuracy in detecting positive and negative classes and their sub-classes. 22 August 2019 The authors note a correction to the article [1]. Table 5 of the original article is incomplete. Few percentage values are missing. This article presents the corrected version of Table 5.
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https://hal.univ-angers.fr/hal-02512584
Contributeur : Marie-Françoise Gerard <>
Soumis le : jeudi 19 mars 2020 - 17:50:53
Dernière modification le : jeudi 19 mars 2020 - 17:52:17

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Imane El Alaoui, Youssef Gahi, Rochdi Messoussi, Youness Chaabi, Alexis Todoskoff, et al.. A novel adaptable approach for sentiment analysis on big social data. International Journal of Big Data, 2018, 5 (1), ⟨10.1186/s40537-018-0120-0⟩. ⟨hal-02512584⟩

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