Utilizing Large Twitter Corpora to Create Sentiment Lexica
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)
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.