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Twitter sentiment classification for measuring public health concerns.

Xiang Ji1, Soon Ae Chun2, Zhi Wei1

  • 11Department of Computer Science, New Jersey Institute of Technology, Martin Luther King Blvd, Newark, NJ 07102 USA.

Social Network Analysis and Mining
|April 1, 2020
PubMed
Summary
This summary is machine-generated.

Monitoring public concern about epidemics is crucial for public health. This study uses Twitter data and a two-step sentiment analysis with machine learning to track public concern, achieving high accuracy in epidemic and mental health domains.

Keywords:
Automatic sentiment labelingMeasure of concernPublic healthSentiment analysisSentiment classificationSocial analyticsTwitter mining

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Area of Science:

  • Public Health Surveillance
  • Computational Social Science
  • Natural Language Processing

Background:

  • Tracking public concern about epidemics is vital for public health officials.
  • Traditional surveillance systems for public health concerns are costly, have limited coverage, and significant delays.
  • Real-time, global, and cost-effective monitoring of public health concerns is needed.

Purpose of the Study:

  • To address limitations of traditional surveillance by utilizing Twitter data for monitoring public concern about epidemics.
  • To develop and evaluate a two-step sentiment classification approach for measuring public concern from Twitter messages.
  • To identify peaks in public concern and correlate them with epidemic or health news.

Main Methods:

  • Utilized Twitter messages for real-time public health concern monitoring.
  • Developed a two-step sentiment classification: distinguishing Personal vs. News tweets, then Personal Negative vs. Personal Non-Negative tweets.
  • Employed an emotion-oriented, clue-based method for automatic training data generation and trained/tested Machine Learning models, including Naïve Bayes.

Main Results:

  • Achieved high accuracy using a two-step method with a Naïve Bayes classifier for both Epidemic and Mental Health domains.
  • Successfully computed a Measure of Concern (MOC) and its timeline based on classified tweets.
  • Demonstrated the potential to correlate peaks in public concern with news trends.

Conclusions:

  • Twitter data, analyzed via a two-step sentiment classification and Machine Learning, offers a viable, cost-effective alternative for public health concern surveillance.
  • The Naïve Bayes classifier proved effective for monitoring public concern in the Epidemic and Mental Health domains.
  • This approach enables timely identification of public concern trends, aiding public health officials in their response strategies.