Paper: Robust Sentiment Detection on Twitter from Biased and Noisy Data

ACL ID C10-2005
Title Robust Sentiment Detection on Twitter from Biased and Noisy Data
Venue International Conference on Computational Linguistics
Session Poster Session
Year 2010
Authors

In this paper, we propose an approach to automatically detect sentiments on Twit- ter messages (tweets) that explores some characteristics of how tweets are written and meta-information of the words that compose these messages. Moreover, we leverage sources of noisy labels as our training data. These noisy labels were provided by a few sentiment detection websites over twitter data. In our experi- ments, we show that since our features are able to capture a more abstract represen- tation of tweets, our solution is more ef- fective than previous ones and also more robust regarding biased and noisy data, which is the kind of data provided by these sources.