Measuring praise and criticism: Inference of semantic orientation from association
ACM Transactions on Information Systems (TOIS)
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
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Using emoticons to reduce dependency in machine learning techniques for sentiment classification
ACLstudent '05 Proceedings of the ACL Student Research Workshop
Sentiment dictionary for effective detection of web users' opinion
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
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In this paper we present an approach to identify opinion of web users from opinionated texts and to classify web users opinion into positive or negative. Today web users express their opinion using multimodal opinion elements such as opinion phrases, emoticons and short words. These form of multimodal opinion expressions are very popular and are used by a large number of web users to document their opinion. In this paper we use semantic based approach to find users opinion from multimodal opinion elements like phrases, emoticons and short words. Our approach detects these multimodal opinion elements and uses them to obtain semantic orientation scores. These scores are later used to identify users opinion from opinionated texts. Our approach is effective and provides better results compared to other approaches on different data sets comprising of opinion.