AI and opinion mining

Hsinchun Chen, David Zimbra

Research output: Contribution to journalArticle

120 Scopus citations

Abstract

Opinion mining which is a sub discipline within data mining and computational linguistics refers to the computational techniques for extracting, classifying, understanding, and assessing the opinions expressed in various online news sources, social media comments, and other user-generated content is discussed. Frameworks and methods for integrating sentiments and opinions expressed with other computational representations such as interesting topics or product features extracted from user-generated text, participant reply networks, spikes and outbreaks of ideas or events are also critically needed. Disagreement and subjectivity also held significant relationships with volatility, where less disagreement and high levels of subjectivity predicted periods of high stock volatility. Positive sentiment reduces trading volume, perhaps because satisfied shareholders hold their stock, while negative sentiment induces trading activity as shareholders defect.

Original languageEnglish (US)
Article number5475086
Pages (from-to)74-76
Number of pages3
JournalIEEE Intelligent Systems
Volume25
Issue number3
DOIs
StatePublished - Aug 4 2010

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Artificial Intelligence

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