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

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)peer-review

13 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.

Original languageEnglish
Title of host publication2016 International Conference on Big Data and Smart Computing, BigComp 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages215-222
Number of pages8
ISBN (Electronic)9781467387965
DOIs
Publication statusPublished - 3 Mar 2016
Externally publishedYes
EventInternational Conference on Big Data and Smart Computing, BigComp 2016 - Hong Kong, China
Duration: 18 Jan 201620 Jan 2016

Publication series

Name2016 International Conference on Big Data and Smart Computing, BigComp 2016

Conference

ConferenceInternational Conference on Big Data and Smart Computing, BigComp 2016
CountryChina
CityHong Kong
Period18/01/1620/01/16

Keywords

  • Emotional concentration
  • Multi-label classification
  • Sentiment analysis

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