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
Using twitter data and sentiment analysis to study diseases dynamics
CARCHIOLO, Vincenza;MALGERI, Michele Giuseppe
2015-01-01
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 diseasesFile in questo prodotto:
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