A Fuzzy Approach to Sentiment Analysis at the Sentence Level

Orestes Appel, Francisco Chiclana, Jenny Carter, Hamido Fujita

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

The objective of this chapter is to present a hybrid approach to the Sentiment Analysis problem focused on sentences or snippets. This new method is centred around a sentiment lexicon enhanced with the assistance of SentiWordNet and fuzzy sets to estimate the semantic orientation polarity and intensity for sentences. This provides a foundation for computing with sentiments. The proposed hybrid method is applied to three different datasets and the results achieved are compared to those obtained using Naïve Bayes (NB) and Maximum Entropy (ME) techniques. It is demonstrated through experimentation that this hybrid approach is more accurate and precise than both NB and ME techniques. Furthermore, it is shown that when applied to datasets containing snippets, the proposed method performs similar to state-of-the-art techniques.
Original languageEnglish
Title of host publicationFuzzy Logic
Subtitle of host publicationRecent Applications and Developments
EditorsJenny Carter, Francisco Chiclana, Arjab Singh Khuman, Tianhua Chen
Place of PublicationCham
PublisherSpringer Nature Switzerland AG
Chapter2
Pages11-34
Number of pages24
Edition1
ISBN (Electronic)9783030664749
ISBN (Print)9783030664732, 9783030664763
DOIs
Publication statusPublished - 24 May 2021

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