Sentiment Classification by Combining Triplet Belief Functions

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

Abstract

Sentiment analysis is an emerging technique that caters for semantic orientation and opinion mining. It is increasingly used to anal- yse online product reviews for identifying customers’ opinions and atti- tudes to products or services in order to improve business performance of companies. This paper presents an innovative approach to combining outputs of sentiment classifiers under the framework of belief functions. The approach is composed of the formulation of outputs of sentiment classifiers in the triplet structure and adoption of its formulas to combin- ing simple support functions derived from triplet functions by evidential combination rules. The empirical studies have been conducted on the performance of sentiment classification individually and in combination, the experimental results show that the best combined classifiers made by these combination rules outperform the best individual classifiers over the MP3 and Movie-Review datasets.
Original languageEnglish
Title of host publicationUnknown Host Publication
PublisherSpringer
Pages234-245
Number of pages12
Volume8793
Publication statusPublished - 16 Oct 2014
EventInternational Conference on Knowledge Science, Engineering and Management 2014 - Romania
Duration: 16 Oct 2014 → …

Conference

ConferenceInternational Conference on Knowledge Science, Engineering and Management 2014
Period16/10/14 → …

Keywords

  • Sentiment analysis
  • opinion mining
  • triplet belief functions and combination rules

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