Analysis of Machine Learning Approaches for Sentiment Analysis of Twitter Data

Abstract

For the past few years, sentiment analysis has been growing rapidly and with the abundance of computation power and plethora of machine learning algorithms, sentiment analysis has found numerous applications and acceptance as research area in machine learning. This paper covers analysis of sentiment analysis dealing with different aspects of its applications such as customer reviews, product reviews, film reviews, emotion detection, market research or many more such areas. To conduct sentiment analysis, data is extracted from various social media platforms like Twitter, Facebook etc. The data available on these social media platforms is primarily unstructured, therefore to analyze this data it must be pre-processed, feature vector identified and further implementation of models to trained and tested on different algorithms. There are several algorithms such as SVM, Naïve Bayes, K-means, KNN, decision tree, random forest and other algorithms, which are used to evaluate and hybrid to improve the efficiency and accuracy of the model.