Elements of information theory
Elements of information theory
Document clustering using word clusters via the information bottleneck method
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
Measuring praise and criticism: Inference of semantic orientation from association
ACM Transactions on Information Systems (TOIS)
Predicting the semantic orientation of adjectives
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Effects of adjective orientation and gradability on sentence subjectivity
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Opinion observer: analyzing and comparing opinions on the Web
WWW '05 Proceedings of the 14th international conference on World Wide Web
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Evaluating WordNet-based Measures of Lexical Semantic Relatedness
Computational Linguistics
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
A novel scheme for domain-transfer problem in the context of sentiment analysis
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Using unlabeled data to handle domain-transfer problem of semantic detection
Proceedings of the 2008 ACM symposium on Applied computing
Modeling online reviews with multi-grain topic models
Proceedings of the 17th international conference on World Wide Web
Hidden sentiment association in chinese web opinion mining
Proceedings of the 17th international conference on World Wide Web
Mining opinion features in customer reviews
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Automatic seed word selection for unsupervised sentiment classification of Chinese text
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Fully automatic lexicon expansion for domain-oriented sentiment analysis
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
SST '06 Proceedings of the Workshop on Sentiment and Subjectivity in Text
Experiments on summary-based opinion classification
CAAGET '10 Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text
SMUC '10 Proceedings of the 2nd international workshop on Search and mining user-generated contents
Opinion target extraction in Chinese news comments
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Unsupervised lexicon induction for clause-level detection of evaluations
Natural Language Engineering
Aspect and sentiment extraction based on information-theoretic co-clustering
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
Extracting chinese product features: representing a sequence by a set of skip-bigrams
CLSW'12 Proceedings of the 13th Chinese conference on Chinese Lexical Semantics
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With the in-depth study of sentiment analysis research, finer-grained opinion mining, which aims to detect opinions on different review features as opposed to the whole review level, has been receiving more and more attention in the sentiment analysis research community recently. Most of existing approaches rely mainly on the template extraction to identify the explicit relatedness between product feature and opinion terms, which is insufficient to detect the implicit review features and mine the hidden sentiment association in reviews, which satisfies (1) the review features are not appear explicit in the review sentences; (2) it can be deduced by the opinion words in its context. From an information theoretic point of view, this paper proposed an iterative reinforcement framework based on the improved information bottleneck algorithm to address such problem. More specifically, the approach clusters product features and opinion words simultaneously and iteratively by fusing both their semantic information and co-occurrence information. The experimental results demonstrate that our approach outperforms the template extraction based approaches.