Twitter Sentiment Analysis: The Good the Bad and the OMG!

Kouloumpis, Efthymios (i-sieve Technologies) | Wilson, Theresa (Johns Hopkins University) | Moore, Johanna (University of Edinburgh)

AAAI Conferences 

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 supervied approach to the problem, but leverage existing hashtags in the Twitter data for building training data.

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