Abstract
In this paper, we investigate the utility of linguistic features for detecting the sentiment of Twitter messages. We evaluate the usefulness of existing lexical resources as well as features that capture information about the informal and creative language used in microblogging. We take a supervised approach to the problem, but leverage existing hashtags in the Twitter data for building training data.
Cite
CITATION STYLE
Kouloumpis, E., Wilson, T., & Moore, J. (2011). Twitter Sentiment Analysis: The Good the Bad and the OMG! In Proceedings of the 5th International AAAI Conference on Weblogs and Social Media, ICWSM 2011 (pp. 538–541). AAAI Press. https://doi.org/10.1609/icwsm.v5i1.14185
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