Selection of optimal hyper-parameter values of support vector machine for sentiment analysis tasks using nature-inspired optimization methods

Sentiment analysis and classification task is used in recommender systems to analyze movie reviews, tweets, Facebook posts, online product blogs, discussion forums, comments social networks. Usually, the performed using supervised machine learning methods such as support vector (SVM) classifier, which have many distinct parameters. The selection of values for these parameters can greatly influence accuracy be addressed an optimization problem. Here we use three heuristics, nature-inspired techniques, cuckoo search (CSO), ant lion optimizer (ALO), polar bear (PBO), parameter tuning SVM models various kernel functions. We validate our approach sentiment Twitter dataset. results are compared metric Nemenyi test.