Learning-Based Method with Valence Shifters for Sentiment Analysis

Ruihua Cheng, Ji Meng Loh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Automatic sentiment classification is becoming a popular and effective way to help online users or companies process and make sense of customer reviews. In this article, a learning-based method for classification of online reviews that achieves better classification accuracy is obtained by (a) combining valence shifters and opinion words into bigrams for use as features in an ordinal margin classifier and (b) using relational information between unigrams/bigrams in the form of a graph to constrain the parameters of the classifier. By using these two components, it is possible to extract more information present in the unstructured data than other methods such as support vector machines and random forest, hence gaining the potential of better classification performance. Indeed, our simulation results show a higher classification accuracy on empirical real data with ground truth and on simulated data.

Original languageEnglish (US)
Title of host publicationProceeding - 17th IEEE International Conference on Data Mining Workshops, ICDMW 2017
EditorsRaju Gottumukkala, George Karypis, Vijay Raghavan, Xindong Wu, Lucio Miele, Srinivas Aluru, Xia Ning, Guozhu Dong
PublisherIEEE Computer Society
Pages357-364
Number of pages8
ISBN (Electronic)9781538614808
DOIs
StatePublished - Dec 15 2017
Event17th IEEE International Conference on Data Mining Workshops, ICDMW 2017 - New Orleans, United States
Duration: Nov 18 2017Nov 21 2017

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
Volume2017-November
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259

Other

Other17th IEEE International Conference on Data Mining Workshops, ICDMW 2017
CountryUnited States
CityNew Orleans
Period11/18/1711/21/17

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Software

Keywords

  • Graph-based learning
  • Ordinal classifier
  • Sentiment analysis

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