Mining Insights from Esports Game Reviews with an Aspect-Based Sentiment Analysis Framework

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 due to 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 to analyze esports player reviews. It incorporates two key components: topic modeling and sentiment analysis. Utilizing the Latent Dirichlet Allocation (LDA) algorithm, the framework effectively identifies diverse topics within the reviews. These identified topics are subsequently employed in a prevalence analysis to uncover associations between players' concerns and various esports games. Moreover, it leverages the cutting-edge Bidirectional Encoder Representations from Transformers (BERT) in conjunction with a Transformer (TFM) downstream layer, enabling the accurate detection of players' sentiments towards the 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 demonstrate that the proposed framework can efficiently identify players' concerns and reveal interesting keywords underlie their reviews. As a result, 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.