WordNet: a lexical database for English
Communications of the ACM
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
An effective statistical approach to blog post opinion retrieval
Proceedings of the 17th ACM conference on Information and knowledge management
Mining opinion features in customer reviews
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Characterizing debate performance via aggregated twitter sentiment
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Enhanced sentiment learning using Twitter hashtags and smileys
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Sentiment analysis of Twitter data
LSM '11 Proceedings of the Workshop on Languages in Social Media
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Twitter is a micro blogging website, where users can post messages in very short text called Tweets. Tweets contain user opinion and sentiment towards an object or person. This sentiment information is very useful in various aspects for business and governments. In this paper, we present a method which performs the task of tweet sentiment identification using a corpus of pre-annotated tweets. We present a sentiment scoring function which uses prior information to classify (binary classification) and weight various sentiment bearing words/phrases in tweets. Using this scoring function we achieve classification accuracy of 87% on Stanford Dataset and 88% on Mejaj dataset. Using supervised machine learning approach, we achieve classification accuracy of 88% on Stanford dataset.