Selection of Optimal Hyperparameter Values of Support Vector Machine for Sentiment Analysis Tasks Using Nature-Inspired Optimisation Methods

Seifedine Kadry

Abstract


Sentiment analysis and classification task is used in recommender systems to analyse movie reviews, tweets, Facebook posts, online product reviews, blogs, discussion forums, and online comments in social networks. Usually, the classification is performed using supervised machine learning methods such as Support Vector Machine (SVM) classifier, which have many distinct parameters. The selection of the values for these parameters can greatly influence the classification accuracy and can be addressed as an optimisation problem. Here we analyze the use of three heuristic, nature-inspired optimization techniques, Cuckoo Search Optimisation (CSO), Ant Lion Optimisation (ALO), and Polar Bear Optimisation (PBO), for parameter tuning of SVM models using various kernel functions. We validate our approach for the sentiment classification task of Twitter dataset. The results are compared using classification accuracy metric and the Nemenyi test.



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