Idea Transcript
Unsupervised Twitter Sentiment Classification Mihaela Dinsoreanu, Andrei Bacu Abstract Sentiment classification is not a new topic but data sources having different characteristics require customized methods to exploit the hidden existing semantic while minimizing the noise and irrelevant information. Twitter represents a huge pool of data having specific features. We propose therefore an unsupervised, domain-independent approach, for sentiment classification on Twitter. The proposed approach integrates NLP techniques, Word Sense Disambiguation and unsupervised rule-based classification. The method is able to differentiate between positive, negative, and objective (neutral) polarities for every word, given the context in which it occurs. Finally, the overall tweet polarity decision is taken by our proposed rulebased classifier. 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Paper Citation in Harvard Style Dinsoreanu M. and Bacu A. (2014). Unsupervised Twitter Sentiment Classification . In Proceedings of the International Conference on Knowledge Management and Information Sharing - Volume 1: KMIS, (IC3K 2014) ISBN 978-989-758-050-5, pages 220-227. DOI: 10.5220/0005079002200227 in Bibtex Style @conference{kmis14, author={Mihaela Dinsoreanu and Andrei Bacu}, title={Unsupervised Twitter Sentiment Classification}, booktitle={Proceedings of the International Conference on Knowledge Management and Information Sharing - Volume 1: KMIS, (IC3K 2014)}, year={2014}, pages={220-227}, publisher={SciTePress}, organization={INSTICC}, doi={10.5220/0005079002200227}, isbn={978-989-758-050-5}, } in EndNote Style TY - CONF JO - Proceedings of the International Conference on Knowledge Management and Information Sharing - Volume 1: KMIS, (IC3K 2014) TI - Unsupervised Twitter Sentiment Classification SN - 978-989-758-050-5 AU - Dinsoreanu M. AU - Bacu A. PY - 2014 SP - 220 EP - 227 DO - 10.5220/0005079002200227