Name
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 spatio-temporal 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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-22741-2_2 | ITBAM |
Field | DocType | Citations |
Data science,Disease,Terminology,Computer science,Sentiment analysis,Exploit,SNOMED CT | Conference | 3 |
PageRank | References | Authors |
0.45 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vincenza Carchiolo | 1 | 261 | 51.62 |
| Alessandro Longheu | 2 | 142 | 29.98 |
| Michele Malgeri | 3 | 219 | 42.79 |