Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Micro-blogging as online word of mouth branding
CHI '09 Extended Abstracts on Human Factors in Computing Systems
Characterizing debate performance via aggregated twitter sentiment
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Content vs. context for sentiment analysis: a comparative analysis over microblogs
Proceedings of the 23rd ACM conference on Hypertext and social media
Evolving social data mining and affective analysis methodologies, framework and applications
Proceedings of the 16th International Database Engineering & Applications Sysmposium
Identifying purpose behind electoral tweets
Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining
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Microblogging services have nowadays become a very popular communication tool among Internet users. Since millions of users share opinions on different aspects of life everyday, microblogging websites are considered as a credible source for exploring both factual and subjective information. This fact has inspired research in the area of automatic sentiment analysis. In this paper we propose an emotional aware clustering approach which performs sentiment analysis of users tweets on the basis of an emotional dictionary and groups tweets according to the degree they express a specific set of emotions. Experimental evaluations on datasets derived from Twitter prove the efficiency of the proposed approach.