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Transformer les big social data en prévisions - méthodes et technologies : Application à l'analyse de sentiments

Abstract : Extracting public opinion by analyzing Big Social data has grown substantially due to its interactive nature, in real time. In fact, our actions on social media generate digital traces that are closely related to our personal lives and can be used to accompany major events by analysing peoples' behavior. It is in this context that we are particularly interested in Big Data analysis methods. The volume of these daily-generated traces increases exponentially creating massive loads of information, known as big data. Such important volume of information cannot be stored nor dealt with using the conventional tools, and so new tools have emerged to help us cope with the big data challenges. For this, the aim of the first part of this manuscript is to go through the pros and cons of these tools, compare their respective performances and highlight some of its interrelated applications such as health, marketing and politics. Also, we introduce the general context of big data, Hadoop and its different distributions. We provide a comprehensive overview of big data tools and their related applications.The main contribution of this PHD thesis is to propose a generic analysis approach to automatically detect trends on given topics from big social data. Indeed, given a very small set of manually annotated hashtags, the proposed approach transfers information from hashtags known sentiments (positive or negative) to individual words. The resulting lexical resource is a large-scale lexicon of polarity whose efficiency is measured against different tasks of sentiment analysis. The comparison of our method with different paradigms in literature confirms the impact of our method to design accurate sentiment analysis systems. Indeed, our model reaches an overall accuracy of 90.21%, significantly exceeding the current models on social sentiment analysis.
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Submitted on : Thursday, March 7, 2019 - 2:53:06 PM
Last modification on : Tuesday, March 31, 2020 - 3:23:25 PM
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  • HAL Id : tel-02060594, version 1


Imane El Alaoui. Transformer les big social data en prévisions - méthodes et technologies : Application à l'analyse de sentiments. Ingénierie, finance et science [cs.CE]. Université d'Angers; Université Ibn Tofail. Faculté des sciences de Kénitra, 2018. Français. ⟨NNT : 2018ANGE0011⟩. ⟨tel-02060594⟩



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