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Title:Sentiment Analysis of Steam Review Datasets using Naive Bayes and Decision Tree Classifier
Author(s):Zuo, Zhen
Subject(s):Sentiment Analysis
Naive Bayes
Decision Tree
Feature Selection
Supervised Machine Learning
Text mining
Abstract: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.
Issue Date:2018-05-09
Date Available in IDEALS:2018-07-03

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