用注意力BiLSTM提升游戏评论情感分析准确率
Enhancing Game Review Sentiment Classification on Steam Platform with Attention-Based BiLSTM
- 基于注意力机制的双向LSTM模型捕捉评论上下文信息
- 测试集准确率达83%，负面评论召回率90%
- 可视化注意力权重，可解释关键情感词
本文研究使用基于注意力的双向长短期记忆（BiLSTM）模型对Steam平台游戏评论进行情感分类。基于从更大规模评论语料库中采样的5万条评论数据，作者将基于TF-IDF和PyCaret AutoML的传统机器学习基线方法与在PyTorch中实现的深度学习方法进行对比。所提出的BiLSTM+Attention模型采用类别加权交叉熵训练以缓解类别不平衡问题，在测试集上达到83%的准确率和85%的加权F1分数，负面评论召回率达90%。论文还展示了注意力可视化结果，通过突出情感关键词体现模型可解释性。研究结论表明，BiLSTM+Attention模型能有效分析Steam评论中的用户情感，有助于开发者理解玩家反馈。
原文摘要 · Abstract (English)
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.
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