Abstract
Twitter has been recently used to predict and/or monitor real world outcomes, and this is also true for health related topic. In this work, we extract information about diseases from Twitter with spatiotemporal constraints, i.e. considering a specific geographic area during a given period. We exploit the SNOMED-CT terminology to correctly detect medical terms, using sentiment analysis to assess to what extent each disease is perceived by persons. We show our first results for a monitoring tool that allow to study the dynamic of diseases.
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CITATION STYLE
Carchiolo, V., Longheu, A., & Malgeri, M. (2015). Using twitter data and sentiment analysis to study diseases dynamics. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9267, pp. 16–24). Springer Verlag. https://doi.org/10.1007/978-3-319-22741-2_2
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