Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
Sentiment analysis: capturing favorability using natural language processing
Proceedings of the 2nd international conference on Knowledge capture
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
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
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
Proceedings of the 11th international conference on Artificial intelligence and law
The utility of linguistic rules in opinion mining
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
A holistic lexicon-based approach to opinion mining
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Automatic Opinion Extraction from Web Documents
ICCAE '09 Proceedings of the 2009 International Conference on Computer and Automation Engineering
Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web
Management Science
A state of the art opinion mining and its application domains
ICIT '09 Proceedings of the 2009 IEEE International Conference on Industrial Technology
Recognizing stances in ideological on-line debates
CAAGET '10 Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text
Pulse: mining customer opinions from free text
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
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Sentiment analysis refers to a broad range of fields of natural language processing, computational linguistics and text mining. Sentiment classification of reviews and comments has emerged as the most useful application in the area of sentiment analysis. Although sentiment classification generally is carried out at the document level, accurate results require analysis at the sentence level. Bag of words and feature based sentiment are the most popular approaches used by researchers to deal with sentiment classification of opinions about products such as movies, electronics, cars etc. Until recently most classification techniques have considered adjectives, adverbs and nouns as features. This paper proposes a new approach based on verb as an important opinion term particularly in social domains. We extract opinion structures which consider verb as the core element. Sentiment orientation is recognized from sentiments inside of opinion structures and their association with the social issue. Experimental results show that considering verbs improves the performance of sentiment classification.