A Comparative Study of Machine Learning Algorithms for Sentiment Analysis of Game Reviews

Abstract

Sentiment analysis, also known as opinion mining, is the process of analysing a body of text to determine the sentiment expressed by it. In this study, Natural Language Processing techniques and Machine Learning algorithms have been applied to create multiple sentiment analysis models customized for the gaming domain to determine the sentiment of game reviews. The dataset was collected from Steam and Metacritic through the use of web API and web scraping. This was followed by text preprocessing, data labelling, feature extraction and finally model training. In the training phase, the effects of oversampling and hyperparameter tuning on the performance of the models have been evaluated. Through comparison between Support Vector Classifier (SVC), Multi-layer Perceptron Classifier (MLP), Extreme Gradient Boosting Classifier (XGB), Logistic Regression (LR) and Multinomial Naïve Bayes (MNB), it was evident that SVC had the most superior performance.

An Assessment of Digitalization Techniques in Contact Centers and Their Impact on Agent Performance and Well-Being

“…Sentiment analysis categorizes text based on expressed sentiments and analyzes language to identify emotional tones or judgments [5]. Its applications extend from understanding customer sentiments regarding products/services to enhancing the interactions between agents and contact center customers, ultimately improving service quality [6].…”