NB vs. SVM: A Contrastive Study for Sentiment Classification on Two Text Domains

Abstract

Thanks to the availability of websites like Twitter, user-generated content is being published on the Internet every second. Sentiment Classification is one of the most attractive fields in text mining, which classifies reviews into positive and negative classes. Pre-processing is an important goal when these textual contexts are employed through machine learning techniques. Without effective pre-processing methods, inaccurate results will be achieved. This article aims to investigate the role of pre-processing in the Sentiment Classification problem. The main idea in this paper comes from using sampling techniques. This paper suggests classifying the tweets and reviews using supervised classifiers. We applied a set of pre-processing stages consisting of n-grams and samplings on two well-known datasets. Our results are worthwhile for companies to monitor the people's sentiment about their brands and for many other applications. We have provided further evidence to confirm the superiority of our model. Experimental results reveal that the proposed model outperforms the existing methods and can improve the performance of Sentiment Classification in terms of accuracy, precision, recall, and F1 criteria.