Study on sentiment classification strategies based on the fuzzy logic with crow search algorithm

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 such as Twitter, Facebook, and Amazon. This paper proposes a novel classification method according fuzzy rule-based system (FRBS) with crow search algorithm (CSA). FRBS is used classify polarity sentences documents, CSA employed optimize best output from logic algorithm. The applied extract its into negative, neutral, positive. Sometimes, outputs must be enhanced, especially since many variables are present rules between them overlap. For cases, solve this limitation faced by achieve result. study compares performance proposed model different machine learning algorithms, SVM, maximum entropy, boosting, SWESA. It tests three famous data sets collected Amazon, Yelp, IMDB. Experimental results demonstrate effectiveness competitive in terms accuracy, recall, precision, F–score.