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Analyse des sentiments et des émotions de commentaires complexes en langue française

Abstract : "Sentiment", "opinion" and "emotion" are words really vaguely defined; not even the dictionary seems to be of any help, being it the first to define each of the three by using the remaining two. And yet, the civilised world is heavily affected by opinions: companies need them to understand how to sell their products; people use them to buy the most fitting product and, more generally, to weigh their decisions; researchers exploit them in Artificial Intelligence studies to understand the nature of the human being. Today we can count on a humongous amount of available information, though it’s hard to use it. In fact, the so-called “Big data” are not always structured – especially for certain languages. French research suffers from a lack of readily available resources for tests. In the context of Natural Language Processing, this thesis aims to explore the nature of sentiment and emotion. Some of our contributions to the NLP research community are: creation of new resources for sentiment and emotion analysis, tests and comparisons of several machine learning methods to study the problem from different points of view - classification of online reviews using sentiment polarity, classification of product characteristics using Aspect- Based Sentiment Analysis. Finally, a psycholinguistic study - supported by a machine learning and lexical approaches – on the relation between who judges, the reviewer, and the object that has been judged, the product.
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Submitted on : Monday, July 20, 2020 - 6:52:09 PM
Last modification on : Wednesday, August 5, 2020 - 3:43:20 AM


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  • HAL Id : tel-02903247, version 1


Stefania Pecore. Analyse des sentiments et des émotions de commentaires complexes en langue française. Linguistique. Université de Bretagne Sud, 2019. Français. ⟨NNT : 2019LORIS522⟩. ⟨tel-02903247⟩



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