Enhancing Game Review Sentiment Classification on Steam Platform with Attention-Based BiLSTM
This work provides a sentiment analysis tool for game developers to understand player feedback from Steam reviews, but the approach is incremental as it applies existing deep learning methods to a specific domain.
The paper applies an attention-based BiLSTM model to classify sentiment in Steam game reviews, achieving 83% accuracy and 85% weighted F1-score on a 50,000-review dataset, with 90% recall for negative reviews.
This paper investigates sentiment classification of Steam game reviews using an attention-based Bidirectional Long Short-Term Memory (BiLSTM) model. Using a dataset of 50,000 reviews sampled from a larger Steam review corpus, the authors compare a traditional machine learning baseline based on TF-IDF and PyCaret AutoML with a deep learning approach implemented in PyTorch. The proposed BiLSTM+Attention model is trained with class-weighted cross-entropy to address class imbalance and achieves 83% accuracy and 85% weighted F1-score on the test set, with 90% recall for negative reviews. The paper also presents attention visualizations to show interpretability by highlighting sentiment-bearing words. The study concludes that the BiLSTM+Attention model is effective for analyzing user sentiment in Steam reviews and useful for helping developers understand player feedback.