Fuzzy approach for sentiment analysis

Chris Jefferson, Han Liu, Mihaela Cocea

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

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Abstract

Sentiment analysis aims to identify the polarity of a document through natural language processing, text analysis and computational linguistics. Over the last decade, there has been much focus on sentiment analysis as the data available on-line has grown exponentially to include many sentiment based documents (reviews, feedback, articles). Many approaches consider machine learning techniques or statistical analysis, but there has been little use of the fuzzy classifiers in this field especially considering the ambiguity of language and the suitability of fuzzy approaches to deal with this ambiguity. This paper proposes a fuzzy rule based system for sentiment analysis, which can offer more refined outputs through the use of fuzzy membership degrees. We compare the performance of our proposed approach with commonly used sentiment classifiers (e.g. Decision Trees, Naïve Bayes) which are known to perform well in this task. The experimental results indicate that our fuzzy-based approach performs marginally better than the other algorithms. In addition, the fuzzy approach allows the definition of different degrees of sentiment without the need to use a larger number of classes.
Original languageEnglish
Title of host publication2017 IEEE Conference on Fuzzy Systems (FUZZ-IEE)
PublisherIEEE
Number of pages6
ISBN (Electronic)978-1509060344
ISBN (Print) 978-1509060351
DOIs
Publication statusPublished - 24 Aug 2017
Event2017 IEEE International Conference on Fuzzy Systems - Royal-Continental Hotel, Naples, Italy
Duration: 9 Jul 201712 Jul 2017
https://www.fuzzieee2017.org/index.html

Publication series

NameIEEE FUZZ-IEEE Proceedings Series
PublisherIEEE
ISSN (Electronic)1558-4739

Conference

Conference2017 IEEE International Conference on Fuzzy Systems
Country/TerritoryItaly
CityNaples
Period9/07/1712/07/17
Internet address

Keywords

  • data mining
  • machine learning
  • fuzzy rule based systems
  • text classification
  • sentiment analysis

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