A Fuzzy-Based Approach for Characterization and Identification of Sentiments
Madhav Kindra, Vikrant Dixit, Vedika Gupta · 2021
Sentiment analysis is the way to find subjectivity, polarity, and polarity strength of a piece of text and a way to observe the reviews and opinions of customers and consumers regarding the quality of product in the market and by examining the words which were defined and trained in proposed model with, i.e., dataset; more the number of words, more accurately the model will work. Fuzzy methods were used here as it helps to scale the value with more divisions. It works similar to human brain in reasoning and also indicates about the intermediate stage of any decision. The goal of this chapter is to draw out features from the product reviews and classify reviews into positive, negative, and neutral; basically, this chapter proposes a fuzzy rule-based system for sentiment analysis, which can offer more refined outputs through the use of fuzzy membership degrees. Comparison was made on the basis of performance of common approach with commonly used sentiment analysis classifiers, for example, decision trees, Naïve Bayes, and many more 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.