Building word-emotion mapping dictionary for online news

Yanghui Rao, Xiaojun Quan, Liu Wenyin, Qing Li, Mingliang Chen

Research output: Journal article publicationConference articleAcademic researchpeer-review

6 Citations (Scopus)

Abstract

Sentiment analysis of online documents such as news articles, blogs and microblogs has received increasing attention. We propose an efficient method of automatically building the word-emotion mapping dictionary for social emotion detection. In the dictionary, each word is associated with the distribution on a series of human emotions. In addition, three different pruning strategies are proposed to refine the dictionary. Experiment on the real-world data sets has validated the effectiveness and reliability of the method. Compared with other lexicons, the dictionary generated using our approach is more adaptive for personalized data set, language-independent, fine-grained, and volume-unlimited. The generated dictionary has a wide range of applications, including predicting the emotional distribution of news articles and tracking the change of social emotions on certain events over time.

Original languageEnglish
Pages (from-to)28-39
Number of pages12
JournalCEUR Workshop Proceedings
Volume917
Publication statusPublished - 1 Dec 2012
Externally publishedYes
Event1st International Workshop on Sentiment Discovery from Affective Data 2012, SDAD 2012 - In Conjunction with ECML-PKDD 2012 - Bristol, United Kingdom
Duration: 28 Sep 201228 Sep 2012

Keywords

  • Emotion dictionary
  • Maximum likelihood estimation
  • Social emotion detection

ASJC Scopus subject areas

  • Computer Science(all)

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