Building emotional dictionary for sentiment analysis of online news

Y. Rao, J. Lei, L. Wenyin, Qing Li, M. Chen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

103 Citations (Scopus)

Abstract

Sentiment analysis of online documents such as news articles, blogs and microblogs has received increasing attention in recent years. In this article, we propose an efficient algorithm and three pruning strategies to automatically build a word-level emotional dictionary for social emotion detection. In the dictionary, each word is associated with the distribution on a series of human emotions. In addition, a method based on topic modeling is proposed to construct a topic-level dictionary, where each topic is correlated with social emotions. Experiment on the real-world data sets has validated the effectiveness and reliability of the methods. Compared with other lexicons, the dictionary generated using our approach is language-independent, fine-grained, and volume-unlimited. The generated dictionary has a wide range of applications, including predicting the emotional distribution of news articles, identifying social emotions on certain entities and news events. © 2013 Springer Science+Business Media New York.
Original languageEnglish
Pages (from-to)723-742
Number of pages20
JournalWorld Wide Web
Volume17
Issue number4
DOIs
Publication statusPublished - 1 Jan 2014
Externally publishedYes

Keywords

  • Emotional dictionary
  • Social emotion detection
  • Topic modeling
  • Web 2.0

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

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