A Hybrid Heuristic for the p-Median Problem
Journal of Heuristics
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Manual and automatic evaluation of summaries
AS '02 Proceedings of the ACL-02 Workshop on Automatic Summarization - Volume 4
Movie review mining and summarization
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Topic sentiment mixture: modeling facets and opinions in weblogs
Proceedings of the 16th international conference on World Wide Web
Modeling online reviews with multi-grain topic models
Proceedings of the 17th international conference on World Wide Web
Introduction to Information Retrieval
Introduction to Information Retrieval
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Mining opinion features in customer reviews
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Syntactic constraints on paraphrases extracted from parallel corpora
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Learning with compositional semantics as structural inference for subsentential sentiment analysis
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Labeled LDA: a supervised topic model for credit attribution in multi-labeled corpora
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Optimization-based content selection for opinion summarization
UCNLG+Sum '09 Proceedings of the 2009 Workshop on Language Generation and Summarisation
Dependency tree-based sentiment classification using CRFs with hidden variables
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Robust sentiment detection on Twitter from biased and noisy data
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Enhanced sentiment learning using Twitter hashtags and smileys
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Target-dependent Twitter sentiment classification
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Part-of-speech tagging for Twitter: annotation, features, and experiments
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach
Proceedings of the 20th ACM international conference on Information and knowledge management
Exploiting hybrid contexts for Tweet segmentation
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
A Twitter-based smoking cessation recruitment system
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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Microblogging services, such as Twitter, have become popular channels for people to express their opinions towards a broad range of topics. Twitter generates a huge volume of instant messages (i.e. tweets) carrying users' sentiments and attitudes every minute, which both necessitates automatic opinion summarization and poses great challenges to the summarization system. In this paper, we study the problem of opinion summarization for entities, such as celebrities and brands, in Twitter. We propose an entity-centric topic-based opinion summarization framework, which aims to produce opinion summaries in accordance with topics and remarkably emphasizing the insight behind the opinions. To this end, we first mine topics from #hashtags, the human-annotated semantic tags in tweets. We integrate the #hashtags as weakly supervised information into topic modeling algorithms to obtain better interpretation and representation for calculating the similarity among them, and adopt Affinity Propagation algorithm to group #hashtags into coherent topics. Subsequently, we use templates generalized from paraphrasing to identify tweets with deep insights, which reveal reasons, express demands or reflect viewpoints. Afterwards, we develop a target (i.e. entity) dependent sentiment classification approach to identifying the opinion towards a given target (i.e. entity) of tweets. Finally, the opinion summary is generated through integrating information from dimensions of topic, opinion and insight, as well as other factors (e.g. topic relevancy, redundancy and language styles) in an unified optimization framework. We conduct extensive experiments on a real-life data set to evaluate the performance of individual opinion summarization modules as well as the quality of the produced summary. The promising experiment results show the effectiveness of the proposed framework and algorithms.