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From opinion classification to recommendations: How texts from a social network can help De la classification d'opinion à la recommandation : l'apport des textes communautaires

Author(s): Damien Poirier | Françoise Fessant | Isabelle Tellier

Journal: Traitement Automatique des Langues
ISSN 1248-9433

Volume: 51;
Issue: 3;
Start page: 19;
Date: 2011;
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Keywords: Opinion classification | Supervised learning | Texts from social networks | Cyberlangage | Recommendation | Collaborative filtering | Cold-Start

This paper is about opinion classification of posts from a social networks by supervised machine learning, in order to use them in a recommender system. We compare different pre-processings, representations and machine learning tools on real data about movies having specificities (very short texts in English, containing a lot of sms-like codes, abbreviations, misspelling...). We study in detail the results of different classifiers and the contribution of the pre-processings on this kind of data. Finally, we evaluate the best classifier with a recommender system based on collaborative filtering.
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