Identification Semi-Automatique de Mots-Germes pour l'Analyse de Sentiments et son Intensité

Abstract : For the purpose of opinion exploring in tweets, this article presents a sentiment classification of tweets content. First, we present a method to identify new sentiment similarity seed words. These seed words are used for predicting sentiment intensity of other words and short phrases in co-occurrence. Then, for testing sentiment similarity, we use: Similarity Measures methods between words and cosine similarity measure between the word embedding representations (e.g. word2vec, GloVE). The experiments results highlight the importance of adapted for tweets seed words. In addition of the corpora size and its pre-treatement. As a conclusion, best results were achieved using cosine similarity measure between the word embedding representations.
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Amal Htait, Sébastien Fournier, Patrice Bellot. Identification Semi-Automatique de Mots-Germes pour l'Analyse de Sentiments et son Intensité. CORIA, Mar 2017, Marseille, France. ⟨hal-01771644⟩

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