Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Topic sentiment mixture: modeling facets and opinions in weblogs
Proceedings of the 16th international conference on World Wide Web
Rated aspect summarization of short comments
Proceedings of the 18th international conference on World wide web
How opinions are received by online communities: a case study on amazon.com helpfulness votes
Proceedings of the 18th international conference on World wide web
Sentiment summarization: evaluating and learning user preferences
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Automatically assessing review helpfulness
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Finding maximal cliques in massive networks by H*-graph
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Detecting product review spammers using rating behaviors
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Opinosis: a graph-based approach to abstractive summarization of highly redundant opinions
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Selecting a comprehensive set of reviews
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
User Features and Social Networks for Topic Modeling in Online Social Media
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
A Game Theoretic Framework for Community Detection
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
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In this paper, we propose a new framework for opinion summarization based on sentence selection. Our goal is to assist users to get helpful opinion suggestions from reviews by only reading a short summary with few informative sentences, where the quality of summary is evaluated in terms of both aspect coverage and viewpoints preservation. More specifically, we formulate the informative-sentence selection problem in opinion summarization as a community-leader detection problem, where a community consists of a cluster of sentences towards the same aspect of an entity. The detected leaders of the communities can be considered as the most informative sentences of the corresponding aspect, while informativeness of a sentence is defined by its informativeness within both its community and the document it belongs to. Review data from six product domains from Amazon.com are used to verify the effectiveness of our method for opinion summarization.