Imbalanced Twitter Sentiment Analysis using Minority Oversampling

Abstract

Micro-Blogging platforms have become one of the popular medium which reflects opinion/sentiment of social events and entities. Machine learning based sentiment analyses have been proven to be successful in finding people's opinion using redundantly available data. However, current study has pointed out that the data being used to train such machine learning models could be highly imbalanced. In the current study live tweets from Twitter have been used to systematically study the effect of class imbalance problem in sentiment analysis. Minority oversampling method is employed here to manage the imbalanced class problem. Two well-known classifiers Support Vector Machine and Multinomial Naïve Bayes have been used for classifying tweets into positive or negative sentiment classes. Results have revealed that minority oversampling based methods can overcome the imbalanced class problem to a greater extent.