Document Type
Dissertation
Disciplines
1.2 COMPUTER AND INFORMATION SCIENCE, Computer Sciences
Abstract
As the online world evolves and new media emerge, consumers are sharing their reviews and opinions online. This has been studied in various academic fields, including marketing and computer science. Sentiment analysis, a technique used to identify the sentiment of a piece of text, has been researched in different domains such as movie reviews and mobile app ratings. However, the video game industry has received relatively little research on experiential products. The purpose of this study is to apply sentiment analysis to user reviews of games on Steam, a popular gaming platform, in order to produce actionable results. The video game industry is a major contributor to the entertainment industry’s revenue and customer feedback is crucial for game developers. Sentiment analysis is widely used by companies to discover what customers are saying about their products. This paper proposes a process for evaluating video game acceptance using game user reviews through the application of sentiment analysis techniques. The focus of this study is to examine the performance of different Text Transformer techniques in the context of text mining when applied to Steam game reviews, using an Support Vector Machine classifier. The goal is to compare the effectiveness of these methods for predicting sentiment, and to develop software that can accurately predict sentiment and explain the prediction through text highlighting. Specifically, the study aims to compare a sentiment analysis classifier based on the traditional TF-IDF text feature representation method to classifiers using the more recent BERT and SBERT techniques. The ultimate goal is to develop a more clear and accurate sentiment prediction tool.
Recommended Citation
Jamshidian, M. (2022). Classifying Sentiment of Reviews by Using TF-IDF, BERT (word embedding), SBERT (sentence embedding) with Support Vector Machine Evaluation. [Technological University Dublin].
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Publication Details
A dissertation submitted in partial fulfilment of the requirements of Technological University Dublin for the degree of M.Sc. in Computing (Data Science) January 2023.