Weighted multi-label classification model for sentiment analysis of online news

Xin LI, Haoran XIE, Yanghui RAO, Yanjia CHEN, Xuebo LIU, Huan HUANG, Fu Lee WANG

Research output: Chapter in Book/Report/Conference proceedingChapter

12 Citations (Scopus)

Abstract

With the extensive growth of social media services, many users express their feelings and opinions through news articles, blogs and tweets/microblogs. To discover the connections between emotions evoked in a user by varied-scale documents effectively, the paper is concerned with the problem of sentiment analysis over online news. Different from previous models which treat training documents uniformly, a weighted multi-label classification model (WMCM) is proposed by introducing the concept of emotional concentration to estimate the weight of training documents, in addition to tackle the issue of noisy samples for each emotion. The topic assignment is also used to distinguish different emotional senses of the same word at the semantic level. Experimental evaluations using short news headlines and long documents validate the effectiveness of the proposed WMCM for sentiment prediction. Copyright © 2016 IEEE.

Original languageEnglish
Title of host publication2016 International Conference on Big Data and Smart Computing, BigComp 2016
Place of PublicationPiscataway, New Jersey
PublisherInstitute of Electrical and Electronics Engineers, Inc
Pages215-222
ISBN (Electronic)9781467387965, 1467387967
DOIs
Publication statusPublished - Mar 2016

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Labels
Blogs
Semantics

Citation

Li, X., Xie, H., Rao, Y., Chen, Y., Liu, X., Huang, H., & Wang, F. L. (2016). Weighted multi-label classification model for sentiment analysis of online news. In 2016 International Conference on Big Data and Smart Computing, BigComp 2016 (pp. 215-222). Piscataway, New Jersey: IEEE.

Keywords

  • Sentiment analysis
  • Emotional concentration
  • Multi-label classification