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.
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Appel, O., Chiclana, F., Carter, J., Fujita, H. (2021). A Fuzzy Approach to Sentiment Analysis at the Sentence Level. In: Carter, J., Chiclana, F., Khuman, A.S., Chen, T. (eds) Fuzzy Logic. Springer, Cham. https://doi.org/10.1007/978-3-030-66474-9_2
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