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International audience; Handling long queries can involve either reducing its size by retaining only useful sentences, or decomposing the long query into several short queries based on their content. A proper sentence classification improves the utility of these procedures. Can Sentiment Analysis has a role in sentence classification? This paper analysis the correlation between sentiment analysis and sentence classification in long book-search queries. Also, it studies the similarity in writing style between book reviews and sentences in book-search queries. To accomplish this study, a semi-supervised method for sentiment intensity prediction, and a language model based on book reviews are presented. In addition to graphical illustrations reflecting the feedback of this study, followed by interpretations and conclusions.

International audience; 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.; Dans le but d'exploiter les opinions dans les tweets, cet article présente une classification à partir du sentiment contenu au sein des tweets. Nous présentons une méthode d'identifi-cation de nouveaux mots-germes. Ils sont utilisés pour la prédiction de l'intensité de sentiments des mots en co-occurrence avec ces mots-germes. Ensuite, le calcul de similarités entre sentiments est appliqué en utilisant: la mesure de la similarité entre deux mots et l'utilisation de plongement de mots (e.g. word2vec, GloVE) couplé à la mesure cosinus. Les résultats montrent l'importance de l'utilisation de mots-germes adaptés aux tweets, ainsi que la taille et le prétrai-tement de corpus. Pour conclure, nous avons obtenu les meilleurs résultats grâce à l'application de la méthode utilisant le plongement de mots couplée à la mesure cosinus. 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. MOTS-CLÉS : Mots-germes, Twitter, Mesure de la Similarité, Plongement de mot, Word2vec, GloVe.

International audience