Subjectivity analysis using machine learning algorithm

Abstract

This paper investigates a new approach of finding sentence level subjectivity analysis using different machine learning algorithms. Along with subjectivity analysis sentiment analysis has also been shown separately in this work. Three different machine learning algorithms - SVM, Naïve Bayes and MLP have been used both for subjectivity and sentiment analysis. Moreover four different classifiers of Naïve Bayes and three different kernels of SVM have been used in this work to analyze the difference in accuracy as well as to find the best outcome among all the experiments. For subjectivity analysis rotten tomato imdb movie review [1] dataset has being used and for sentiment analysis acl imdb movie review [2] dataset has been used. Lastly, the impact of stop words and number of attributes in accuracy both for subjectivity and sentiment analysis has also been illustrated.