Utilizing Large Twitter Corpora to Create Sentiment Lexica

Abstract

The paper describes an automatic Twitter sentiment lexicon creator and a lexicon-based sentiment analysis system. The lexicon creator is based on a Pointwise Mutual Information approach, utilizing 6.25 million automatically labeled tweets and 103 million unlabeled, with the created lexicon consisting of about 3 000 entries. In a comparison experiment, this lexicon beat a manually annotated lexicon. A sentiment analysis system utilizing the created lexicon, and handling both negation and intensification, produces results almost on par with sophisticated machine learning-based systems, while significantly outperforming those in terms of run-time.

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