Sentiment Analysis of Steam Review Datasets using Naive Bayes and Decision Tree Classifier
Zhen C. Zuo · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign) · 2018
Sentiment analysis or opinion mining is one of the major topics in Natural Language Processing and Text Mining. This paper will provide a complete process of sentiment analysis from data gathering and data preparation to final classification on a user-generated sentimental dataset with Naive Bayes and Decision Tree classifiers. The dataset used for analysis is the product reviews from Steam, a digital distribution platform. The performance of different feature selection models and classifiers will be compared. The trained classifier can be used to make prediction for unlabeled reviews and help companies to increase potential profits in global digital product market.