用LSTM分析手游评论情感，准确率超90%。
Sentiment Analysis of Mobile Legends App Reviews Using Machine Learning and LSTM-Based Deep Learning Models
- 用LSTM捕捉文本顺序依赖，比传统机器学习更强。
- 在1万条标注评论上，准确率达92%，F1-score为91%。
- 适合想用深度学习分析用户评价的开发者或产品经理。
本文对比了机器学习与基于LSTM的深度学习方法在Mobile Legends应用评论情感分析中的表现。基于包含10,000条标注为正面、负面和中性评论的数据集，研究评估了使用TF-IDF和PyCaret AutoML的传统模型，并与专门设计用于捕捉文本序列依赖关系的LSTM模型进行比较。结果显示，LSTM模型优于经典机器学习基线，达到92%的准确率和91%的加权F1-score。结果表明，深度学习在处理非正式且依赖上下文的用户评论文本方面更具优势。
原文摘要 · Abstract (English)
This paper compares Machine Learning and LSTM-based Deep Learning methods for sentiment analysis of Mobile Legends app reviews. Using a dataset of 10,000 reviews labeled as positive, negative, and neutral, the study evaluates traditional models with TF-IDF and PyCaret AutoML and compares them against an LSTM model designed to capture sequential text dependencies. The results show that the LSTM model outperforms the classical Machine Learning baselines, achieving 92% accuracy and a weighted F1-score of 91%. The findings indicate that deep learning is more effective for handling informal and context-dependent user review text.
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