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

11 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 Sept 201228 Sept 2012

Keywords

  • Emotion dictionary
  • Maximum likelihood estimation
  • Social emotion detection

ASJC Scopus subject areas

  • General Computer Science

Fingerprint

Dive into the research topics of 'Building word-emotion mapping dictionary for online news'. Together they form a unique fingerprint.

Cite this