Enhancing Game Review Sentiment Classification on Steam Platform with Attention-Based BiLSTM
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866909010062475264 |
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| author | Oktarian, Abit Ahmad Wijaya, Fadhil Fitra Luthfi, Dhafin Razaqa Muthoharoh, Luluk Satria, Ardika Manullang, Martin Clinton Tosima |
| author_facet | Oktarian, Abit Ahmad Wijaya, Fadhil Fitra Luthfi, Dhafin Razaqa Muthoharoh, Luluk Satria, Ardika Manullang, Martin Clinton Tosima |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_01315 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Enhancing Game Review Sentiment Classification on Steam Platform with Attention-Based BiLSTM Oktarian, Abit Ahmad Wijaya, Fadhil Fitra Luthfi, Dhafin Razaqa Muthoharoh, Luluk Satria, Ardika Manullang, Martin Clinton Tosima Computation and Language 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. |
| title | Enhancing Game Review Sentiment Classification on Steam Platform with Attention-Based BiLSTM |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2605.01315 |