Mining Insights From Esports Game Reviews With an Aspect-Based Sentiment Analysis Framework

Abstract

The explosive growth of player-versus-player games and tournaments has catapulted esports games into a rapidly expanding force in the gaming industry. However, novice and armature players’ voices are often inadvertently overlooked because of a lack of effective analytical methods, despite the close collaboration between professional esports teams and operators. To ensure the quality of esports game services and establish a balanced gaming environment, it is essential to consider the opinions of unprofessional players and comprehensively analyze their reviews. This study proposes a new framework for analyzing esports reviews of players. It incorporates two key components: topic modeling and sentiment analysis. Utilizing the Latent Dirichlet Allocation (LDA) algorithm, the framework effectively identifies diverse topics within reviews. These identified topics were subsequently employed in a prevalence analysis to uncover the associations between players’ concerns and various esports games. Moreover, it leverages cutting-edge Bidirectional Encoder Representations from Transformers (BERT) in conjunction with a Transformer (TFM) downstream layer, enabling accurate detection of players’ sentiments toward different topics. We experimented using a dataset containing 1.6 million English reviews collected up to December 2021 for four esports games on Steam: TEKKEN7, Dota2, PUBG, and CS:GO. The experimental results demonstrated that the proposed framework can efficiently identify players’ concerns and reveal interesting keywords underlying their reviews. Consequently, it provides precise insights and valuable customer feedback to esports game operators, enabling them to enhance their services and provide an improved gaming experience for all players.

Keywords

• Esports
• topic modeling
• prevalence analysis
• sentiment analysis
• steam