De la Classification d'Opinion à la Recommandation : l'Apport des Textes Communautaires

Cet article s'interesse a la classification d'opinions de textes communautaires par apprentissage supervise, en vue de les utiliser pour un systeme de recommandation. Nous comparons differents pretraitements, representations et techniques d'apprentissage sur des donnees reelles parlant de films et presentant diverses particularites (textes tres courts en anglais, contenant beaucoup de codes type sms, d'abreviations, de fautes d'orthographe, etc.). Nous etudions en details les resultats de differents classifieurs ainsi que l'apport des pretraitements sur ce type de donnees. Pour finir, nous evaluons les resultats du meilleur classifieur a l'aide d'un moteur de recommandation de type filtrage collaboratif.

[1]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[2]  Gérard Dray,et al.  Is a Voting Approach Accurate for Opinion Mining? , 2008, DaWaK.

[3]  Cane Wing-ki Leung,et al.  Integrating Collaborative Filtering and Sentiment Analysis: A Rating Inference Approach , 2006 .

[4]  Hsin-Hsi Chen,et al.  Overview of Opinion Analysis Pilot Task at NTCIR-6 , 2007, NTCIR.

[5]  Cécile Bothorel,et al.  Approches Statistique et Linguistique Pour la Classification de Textes d'Opinion Portant sur les Films , 2009, Fouille de Données d'Opinions.

[6]  Rachel Panckhurst.  Le discours électronique médié : bilan et perspectives. , 2006 .

[7]  Michael J. Pazzani,et al.  Content-Based Recommendation Systems , 2007, The Adaptive Web.

[8]  Janyce Wiebe,et al.  Just How Mad Are You? Finding Strong and Weak Opinion Clauses , 2004, AAAI.

[9]  Craig MacDonald,et al.  Overview of the TREC 2007 Blog Track , 2007, TREC.

[10]  Thorsten Joachims,et al.  Learning to classify text using support vector machines - methods, theory and algorithms , 2002, The Kluwer international series in engineering and computer science.

[11]  Marc Boullé,et al.  MODL: A Bayes optimal discretization method for continuous attributes , 2006, Machine Learning.

[12]  Yehuda Koren,et al.  Factor in the neighbors: Scalable and accurate collaborative filtering , 2010, TKDD.

[13]  Hsin-Hsi Chen,et al.  Overview of Multilingual Opinion Analysis Task at NTCIR-7 , 2008, NTCIR.

[14]  Luca Dini,et al.  Classification d'opinions par mthodes symbolique, statistique et hybride , 2007 .

[15]  Éric Gaussier,et al.  Apport des données thématiques dans les systèmes de recommandation : hybridation et démarrage à froid , 2011, EGC.

[16]  Robin D. Burke,et al.  Hybrid Web Recommender Systems , 2007, The Adaptive Web.

[17]  Hong Yu,et al.  Towards Answering Opinion Questions: Separating Facts from Opinions and Identifying the Polarity of Opinion Sentences , 2003, EMNLP.

[18]  Craig MacDonald,et al.  Overview of the TREC 2006 Blog Track , 2006, TREC.

[19]  Matthew Hurst,et al.  Towards a Robust Metric of Polarity , 2006, Computing Attitude and Affect in Text.

[20]  Michael J. Pazzani,et al.  A Framework for Collaborative, Content-Based and Demographic Filtering , 1999, Artificial Intelligence Review.

[21]  Bo Pang,et al.  A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts , 2004, ACL.

[22]  Marc Boullé,et al.  Compression-Based Averaging of Selective Naive Bayes Classifiers , 2007, J. Mach. Learn. Res..

[23]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

[24]  Lillian Lee,et al.  Opinion Mining and Sentiment Analysis , 2008, Found. Trends Inf. Retr..

[25]  Ari Rappoport,et al.  Enhanced Sentiment Learning Using Twitter Hashtags and Smileys , 2010, COLING.

[26]  Katarzyna Wegrzyn-Wolska,et al.  RRSS - Rating Reviews Support System Purpose Built for Movies Recommendation , 2007, AWIC.

[27]  Iadh Ounis,et al.  Overview of the TREC 2008 Blog Track , 2008, TREC.

[28]  K. Nageswara Rao,et al.  Application Domain and Functional Classification of Recommender Systems—A Survey , 2008 .

[29]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[30]  Marc Boullé.  A Bayes Optimal Approach for Partitioning the Values of Categorical Attributes , 2005, J. Mach. Learn. Res..

[31]  M. Genereux,et al.  Defi: classification de textes Francais subjectifs , 2007 .

[32]  Freimut Bodendorf,et al.  Swarm Intelligence for Analyzing Opinions in Online Communities , 2010, 2010 43rd Hawaii International Conference on System Sciences.

[33]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[34]  Françoise Fessant,et al.  Designing Specific Weighted Similarity Measures to Improve Collaborative Filtering Systems , 2008, ICDM.

[35]  Thorsten Joachims,et al.  Making large-scale support vector machine learning practical , 1999 .