IMood: Discovering U.S. immigration reform sentiment

Wingyan Chung, Daniel Zeng

Research output: Contribution to conferencePaper

Abstract

Traditionally, public opinion and policy sentiment are gauged through various media, news agencies, and social channels. However, the large volumes and variety of expressions on social media have challenged traditional policy analysis and public sentiment assessment. In this research, we developed a framework for automatic sentiment discovery and network analysis. We built a real-time online system called iMood that extracts user sentiment on U.S. immigration reform. Based on 324,426 tweets posted by 133,998 users on Twitter between May and July 2013, iMood computes scores along eight emotion categories and construct networks of Twitter users. Opinion leaders, influential users, and community activists were identified through the network analysis. iMood displays temporal changes and trends of user sentiment and emotion, helping policy makers to devise strategic plans. The results demonstrate strong potential for supporting sentiment discovery for public policy decision-making.

Original languageEnglish (US)
StatePublished - Jan 1 2013
Externally publishedYes
Event23rd Workshop on Information Technology and Systems: Leveraging Big Data Analytics for Societal Benefits, WITS 2013 - Milan, Italy
Duration: Dec 14 2013Dec 15 2013

Other

Other23rd Workshop on Information Technology and Systems: Leveraging Big Data Analytics for Societal Benefits, WITS 2013
CountryItaly
CityMilan
Period12/14/1312/15/13

Keywords

  • Data visualization
  • IMood
  • Network analysis
  • Public policy informatics
  • Sentiment analysis
  • Social media analytics

ASJC Scopus subject areas

  • Information Systems

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  • Cite this

    Chung, W., & Zeng, D. (2013). IMood: Discovering U.S. immigration reform sentiment. Paper presented at 23rd Workshop on Information Technology and Systems: Leveraging Big Data Analytics for Societal Benefits, WITS 2013, Milan, Italy.