Abstract
Our current work studies sentiment representation in messages posted on health forums. We analyze 11 sentiment representations in a framework of multi-label learning. We use Exact Match and F-score to compare effectiveness of those representations in sentiment classification of a message. Our empirical results show that feature selection can significantly improve Exact Match of the multi-label sentiment classification (paired t-test, P = 0.0024).
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The data set is available upon request at victoria.bobicev@ia.utm.md.
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Bobicev, V., Sokolova, M. (2017). Confused and Thankful: Multi-label Sentiment Classification of Health Forums. In: Mouhoub, M., Langlais, P. (eds) Advances in Artificial Intelligence. Canadian AI 2017. Lecture Notes in Computer Science(), vol 10233. Springer, Cham. https://doi.org/10.1007/978-3-319-57351-9_33
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DOI: https://doi.org/10.1007/978-3-319-57351-9_33
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