Efficient Sentiment Correlation for Large-scale Demographics

Abstract : Analyzing sentiments of demographic groups is becoming important for the Social Web, where millions of users provide opinions on a wide variety of content. While several approaches exist for mining sentiments from product reviews or micro-blogs, little attention has been devoted to aggregating and comparing extracted sentiments for different demographic groups over time, such as 'Students in Italy' or 'Teenagers in Europe'. This problem demands efficient and scalable methods for sentiment aggregation and correlation, which account for the evolution of sentiment values, sentiment bias, and other factors associated with the special characteristics of web data. We propose a scalable approach for sentiment indexing and aggregation that works on multiple time granularities and uses incrementally updateable data structures for online operation. Furthermore, we describe efficient methods for computing meaningful sentiment correlations, which exploit pruning based on demographics and use top-k correlations compression techniques. We present an extensive experimental evaluation with both synthetic and real datasets, demonstrating the effectiveness of our pruning techniques and the efficiency of our solution.
Type de document :
Communication dans un congrès
Kenneth Ross, Divesh Srivastava, Dimitris Papadias. SIGMOD 2013 - Special Interest Group on Management of Data, Jun 2013, New York, NY, United States. ACM, pp.253-264, 2013, 〈10.1145/2463676.2465317〉
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-00923543
Contributeur : Valérie Samper <>
Soumis le : lundi 6 janvier 2014 - 17:11:55
Dernière modification le : mardi 28 octobre 2014 - 18:33:48

Identifiants

Collections

Citation

Mikalai Tsytsarau, Sihem Amer-Yahia, Themis Palpanas. Efficient Sentiment Correlation for Large-scale Demographics. Kenneth Ross, Divesh Srivastava, Dimitris Papadias. SIGMOD 2013 - Special Interest Group on Management of Data, Jun 2013, New York, NY, United States. ACM, pp.253-264, 2013, 〈10.1145/2463676.2465317〉. 〈hal-00923543〉

Partager

Métriques

Consultations de la notice

115