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 language | English |
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Title of host publication | Fuzzy Logic |
Subtitle of host publication | Recent Applications and Developments |
Editors | Jenny Carter, Francisco Chiclana, Arjab Singh Khuman, Tianhua Chen |
Place of Publication | Cham |
Publisher | Springer Nature Switzerland AG |
Chapter | 2 |
Pages | 11-34 |
Number of pages | 24 |
Edition | 1 |
ISBN (Electronic) | 9783030664749 |
ISBN (Print) | 9783030664732, 9783030664763 |
DOIs | |
Publication status | Published - 24 May 2021 |