Review Classification Based on Machine Learning: Classifying Game User Reviews

Zhang Yejian, Shingo Takada

Research output: Contribution to journalArticlepeer-review

Abstract

With the development of the game industry, the maturity of online game sales platforms, and the increasing complexity of game software itself, game companies need to analyze massive amounts of user reviews to understand the hidden defects of the game and the direction of future iterations. Manually reading game reviews is a labor-intensive and time-consuming task, as the number of reviews can go up to several thousand per day. Automatically classifying these game reviews will help alleviate this issue, but traditional classifiers will need a large number of labeled instances for training. In this paper, we propose and implement an approach that combines transfer learning in natural language processing (BERT), unsupervised learning, and active learning to classify game reviews using only a small amount of labeled instances. We found that our approach obtains 88.8% classification accuracy with only 100 labeled training instances. Our implementation can be extended to handle different types of new games by using a small amount of extra labeled instances and manual work.

Original languageEnglish
Pages (from-to)142447-142463
Number of pages17
JournalIEEE Access
Volume11
DOIs
Publication statusPublished - 2023

Keywords

  • Active learning
  • game review classification
  • machine learning
  • natural language processing
  • software engineering

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering

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