Optimization of sentiment analysis using machine learning classifiers
Words and phrases bespeak the perspectives of people about products, services, governments and events on social media. Extricating positive or negative polarities from social media text denominates task of sentiment analysis in the field of natural language processing. The exponential growth of demands for business organizations and governments, impel researchers to accomplish their research in sentiment analysis. This paper leverages four state of the art machine learning classifiers viz. Naive Bayes, J48, BFTree and OneR for optimization of sentiment analysis. The experiments are performed using three manually compiled datasets; two of them are captured from Amazon and one dataset is assembled from IMDB movie reviews. The efficacies of these four classification techniques are examined and compared. The Naive Bayes found to be quite fast in learning whereas OneR seems more promising in generating the accuracy of 91.3% in precision, 97% in F measure and 92.34% in correctly classified instances.
Human centric Computing and Information Sciences