Monitoring public health concerns using twitter sentiment classifications

Xiang Ji, Soon Ae Chun, James Geller

Research output: Chapter in Book/Report/Conference proceedingConference contribution

45 Scopus citations

Abstract

An important task of public health officials is to keep track of spreading epidemics, and the locations and speed with which they appear. Furthermore, there is interest in understanding how concerned the population is about a disease outbreak. Twitter can serve as an important data source to provide this information in real time. In this paper, we focus on sentiment classification of Twitter messages to measure the Degree of Concern (DOC) of the Twitter users. In order to achieve this goal, we develop a novel two-step sentiment classification workflow to automatically identify personal tweets and negative tweets. Based on this workflow, we present an Epidemic Sentiment Monitoring System (ESMOS) that provides tools for visualizing Twitter users' concern towards different diseases. The visual concern map and chart in ESMOS can help public health officials to identify the progression and peaks of concern for a disease in space and time, so that appropriate preventive actions can be taken. The DOC measure is based on the sentiment-based classifications. We compare clue-based and different Machine Learning methods to classify sentiments of Twitter users regarding diseases, first into personal and neutral tweets and then into negative from neutral personal tweets. In our experiments, Multinomial Naïve Bayes achieved overall the best results and took significantly less time to build the classifier than other methods.

Original languageEnglish (US)
Title of host publicationProceedings - 2013 IEEE International Conference on Healthcare Informatics, ICHI 2013
Pages335-344
Number of pages10
DOIs
StatePublished - 2013
Event2013 1st IEEE International Conference on Healthcare Informatics, ICHI 2013 - Philadelphia, PA, United States
Duration: Sep 9 2013Sep 11 2013

Publication series

NameProceedings - 2013 IEEE International Conference on Healthcare Informatics, ICHI 2013

Other

Other2013 1st IEEE International Conference on Healthcare Informatics, ICHI 2013
Country/TerritoryUnited States
CityPhiladelphia, PA
Period9/9/139/11/13

All Science Journal Classification (ASJC) codes

  • Health Informatics

Keywords

  • Epidemics Detection
  • Health Information Visualization
  • Sentiment Analysis
  • Social Network
  • Twitter

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