%0 Journal Article
%T From opinion classification to recommendations: How texts from a social network can help De la classification d'opinion ¨¤ la recommandation : l'apport des textes communautaires
%A Damien Poirier
%A Fran£żoise Fessant
%A Isabelle Tellier
%J Traitement Automatique des Langues
%D 2011
%I Association pour le Traitement Automatique des Langues (ATALA)
%X 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.
%K Opinion classification
%K Supervised learning
%K Texts from social networks
%K Cyberlangage
%K Recommendation
%K Collaborative filtering
%K Cold-Start
%U http://www.atala.org/IMG/pdf/1-Poirier-TAL51-3.pdf