A Machine Learning Approach to Sentiment Analysis on Web Based Feedback

The advent of this new era of technology has brought forward new and convenient ways to express views and opinions. This is a major factor for the vast influx of data that we experience every day. People have found out new ways to communicate their feelings and emotions to others through written texts sent over the Internet. This is exactly where the field of sentiment analysis comes into existence. This paper focuses on analyzing the reviews of various applications on the Internet and to understand whether they are positive or negative. For achieving this objective, we initially pre-process the data by performing data cleaning and removal of stop words. TF-IDF method is used to convert the cleaned data into a vectorised form. Finally, the machine learning algorithms: Naïve Bayes, Support Vector Machine and Logistic Regression are applied and their comparative analysis is performed on the basis of accuracy, precision and recall parameters. Our proposed approach has achieved an accuracy of 92.1% and has outperformed many other existing approaches.