<![CDATA[Purpose – This study develops an interpretable framework for monitoring and forecasting user perception of Steam games by integrating sentiment analysis, topic modelling, short-horizon forecasting, and topic-level attribution. Methods – English-language Steam reviews from fifteen games across five genres were analysed over a twelve-month period. BERT was used to generate review-level sentiment ratings, which were aggregated into a weekly User Perception Score (UPS). BERTopic identified discussion themes, while an expanding-window moving-average model forecast UPS over a four-week horizon. Forecast performance was evaluated through walk-forward cross-validation against naive and damped linear-regression baselines, and topic-level attribution was used to explain recent changes in perception. Findings – The sentiment model showed strong agreement with Steam’s binary voting signal, while the expanding-window forecaster generally produced lower prediction error than the comparison baselines. Genre-level patterns indicated more favourable perception for Simulation, Role-Playing, and Action-Adventure titles, whereas First-Person Shooter and Strategy titles showed more mixed perception. Topic attribution further revealed that changes in both topic sentiment and topic prevalence contributed to shifts in weekly UPS. Research Implications – The framework provides developers and publishers with an interpretable monitoring approach for identifying perception trends and the discussion themes associated with them. Originality – The study combines weekly perception forecasting with a decomposition of topic-level contribution, enabling dynamic and interpretable analysis beyond static sentiment or topic summaries.]]>
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