ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A holistic lexicon-based approach to opinion mining
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Fully automatic lexicon expansion for domain-oriented sentiment analysis
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Automatic construction of a context-aware sentiment lexicon: an optimization approach
Proceedings of the 20th international conference on World wide web
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One approach in opinion mining is to perform sentiment classification at the sentence level. User's view on a discovered product feature is predicted by the opinion words, e.g. adjectives, appeared in the same sentence. A number of previous works has been proposed and these approaches typically treat the feature and word relations identically. Blindly using sentiments of all opinion words to perform classification would lead to false results. In this paper, we investigate the relationship between features and opinion words using the corpus-based approach. We proposed a Feature-Opinion Association (FOA) algorithm to match these two in sentences to improve sentiment analysis results. We construct a feature-based sentiment lexicon using the proposed algorithm in the sentiment identification process. Extensive experiments based on a commercial product review site show that our method is quite effective in obtaining a more accurate result.