Abstract
In recent times, sentiment analysis research has gained wide popularity. That situation causes the importance of online applications that allow users to express their opinions on events, services, or products through social media applications such as Twitter, Facebook, and Amazon. This paper proposes a novel sentiment classification method according to the fuzzy rule-based system (FRBS) with the crow search algorithm (CSA). FRBS is used to classify the polarity of sentences or documents, and the CSA is employed to optimize the best output from the fuzzy logic algorithm. The FRBS is applied to extract the sentiment and classify its polarity into negative, neutral, and positive. Sometimes, the outputs of the FRBS must be enhanced, especially since many variables are present and the rules between them overlap. For such cases, the CSA is used to solve this limitation faced by FRBS to optimize the outputs of FRBS and achieve the best result. This study compares the performance of the proposed model with different machine learning algorithms, such as SVM, maximum entropy, boosting, and SWESA. It tests the model on three famous data sets collected from Amazon, Yelp, and IMDB. Experimental results demonstrate the effectiveness of the proposed model and achieve competitive performance in terms of accuracy, recall, precision, and the F–score.
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