Performance Analysis of Machine Learning Algorithms and Feature Extraction Methods for Sentiment Analysis

Sentiment Analysis is the process of evaluating the document or sentence and assigning it a polarity. It has been a key area of research for the past few years. With the evolution of the World Wide Web, many platforms such as Twitter, Facebook, etc. came up where people can express their emotions related to an object, movie, or any political party. These reviews are read by many people before taking some decision, and hence it is very important for the Sentiment Analysis models to assign polarity to the reviews properly. In this paper, we will be analyzing different existing Machine Learning algorithms such as Linear Regression, Support Vector Machine, Decision Trees, Random Forest, and Maximum Entropy Model used for Sentiment Analysis, along with 2 most used methods of feature extraction, Bag-of-Words(BOW) and Term Frequency- Inverse Document Frequency (TF-IDF). The results showed that BOW used with Linear Regression models shows the best results achieving an accuracy score of 34.83% and takes minimum time for training.