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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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Efthymios Kouloumpis
Theresa Wilson
Johanna D. Moore
Proceedings of the International AAAI Conference on Web and Social Media
Johns Hopkins University
University of Edinburgh
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Analyzing shared references across papers
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Kouloumpis et al. (Tue,) studied this question.
www.synapsesocial.com/papers/6a0a1c30a9b588564434c4c7 — DOI: https://doi.org/10.1609/icwsm.v5i1.14185