Performance analysis of supervised machine learning techniques for sentiment analysis

Abstract

Wide use of internet and web applications like, feedback collection systems are now making peoples smarter. In these applications, peoples used to give their feedback about the movies, products, services, etc through which they have gone, and this feedback are publicly available for future references. It is a tedious task for the machines to identify the feedback types, i:e positive or negative. And here Machine Learning Techniques plays vital roles to train the machine and make it intelligent so that the machine will be able to identify the feedback type which may give more benefits and features for those web applications and the users. There are many supervised machine learning techniques are available so it is a difficult task to choose the best one. In this paper, we have collected the movie review datasets of different sizes and have selected some of the widely used and popular supervised machine learning algorithms, for training the model. So that the model will be able to categorize the review. Python's NLTK package along with the WinPython and Spyder are used for processing the movie reviews. Then Python's sklearn package is used for training the model and finding the accuracy of the model.