Measuring praise and criticism: Inference of semantic orientation from association
ACM Transactions on Information Systems (TOIS)
Accurate methods for the statistics of surprise and coincidence
Computational Linguistics - Special issue on using large corpora: I
Tracking point of view in narrative
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
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
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
ACL '04 Proceedings of the 42nd 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
OpinionFinder: a system for subjectivity analysis
HLT-Demo '05 Proceedings of HLT/EMNLP on Interactive Demonstrations
Topic sentiment mixture: modeling facets and opinions in weblogs
Proceedings of the 16th international conference on World Wide Web
Sentiment analysis in multiple languages: Feature selection for opinion classification in Web forums
ACM Transactions on Information Systems (TOIS)
Hidden sentiment association in chinese web opinion mining
Proceedings of the 17th international conference on World Wide Web
A generation model to unify topic relevance and lexicon-based sentiment for opinion retrieval
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Leveraging Sentiment Analysis for Topic Detection
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Mining opinion features in customer reviews
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Using pointwise mutual information to identify implicit features in customer reviews
ICCPOL'06 Proceedings of the 21st international conference on Computer Processing of Oriental Languages: beyond the orient: the research challenges ahead
Techniques and applications for sentiment analysis
Communications of the ACM
Implicit feature identification via hybrid association rule mining
Expert Systems with Applications: An International Journal
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In sentiment analysis, identifying features associated with an opinion can help produce a finer-grained understanding of online reviews. The vast majority of existing approaches focus on explicit feature identification, few attempts have been made to identify implicit features in reviews. In this paper, we propose a novel two-phase co-occurrence association rule mining approach to identifying implicit features. Specifically, in the first phase of rule generation, for each opinion word occurring in an explicit sentence in the corpus, we mine a significant set of association rules of the form [opinion-word, explicit-feature] from a co-occurrence matrix. In the second phase of rule application, we first cluster the rule consequents (explicit features) to generate more robust rules for each opinion word mentioned above. Given a new opinion word with no explicit feature, we then search a matched list of robust rules, among which the rule having the feature cluster with the highest frequency weight is fired, and accordingly, we assign the representative word of the cluster as the final identified implicit feature. Experimental results show considerable improvements of our approach over other related methods including baseline dictionary lookups, statistical semantic association models, and bi-bipartite reinforcement clustering.