The Journal of Machine Learning Research
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining product reviews based on shallow dependency parsing
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Phrase dependency parsing for opinion mining
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
TwiSent: a multistage system for analyzing sentiment in twitter
Proceedings of the 21st ACM international conference on Information and knowledge management
Incorporating author preference in sentiment rating prediction of reviews
Proceedings of the 22nd international conference on World Wide Web companion
Online debate summarization using topic directed sentiment analysis
Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining
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In this paper, we present a novel approach to identify feature specific expressions of opinion in product reviews with different features and mixed emotions. The objective is realized by identifying a set of potential features in the review and extracting opinion expressions about those features by exploiting their associations. Capitalizing on the view that more closely associated words come together to express an opinion about a certain feature, dependency parsing is used to identify relations between the opinion expressions. The system learns the set of significant relations to be used by dependency parsing and a threshold parameter which allows us to merge closely associated opinion expressions. The data requirement is minimal as this is a one time learning of the domain independent parameters. The associations are represented in the form of a graph which is partitioned to finally retrieve the opinion expression describing the user specified feature. We show that the system achieves a high accuracy across all domains and performs at par with state-of-the-art systems despite its data limitations.