用堆叠模型提升社交媒体情绪识别准确率
Emotion Detection in Reddit: Comparative Study of Machine Learning and Deep Learning Techniques
- 采用堆叠分类器融合多种机器学习模型
- 在GoEmotions数据集上达到最高准确率
- 适合需要高精度情绪分析的实时应用
情绪检测在人类交流中至关重要，显著影响行为、人际关系和决策。本研究基于GoEmotions数据集，对Reddit评论进行文本情绪识别，该数据集标注了27种情绪，并映射至埃克曼六类基本情绪：喜悦、愤怒、恐惧、悲伤、厌恶和惊讶。实验比较了六种机器学习模型、三种集成模型和一个LSTM模型，结果表明堆叠分类器在准确率和性能上表现最优。与预训练模型EmoBERTa对比，堆叠分类器效果更佳。最终，该模型通过Streamlit网页应用部署，展示了其在真实场景中进行文本情绪分析的潜力。
原文摘要 · Abstract (English)
Emotion detection is pivotal in human communication, as it significantly influences behavior, relationships, and decision-making processes. This study concentrates on text-based emotion detection by leveraging the GoEmotions dataset, which annotates Reddit comments with 27 distinct emotions. These emotions are subsequently mapped to Ekman's six basic categories: joy, anger, fear, sadness, disgust, and surprise. We employed a range of models for this task, including six machine learning models, three ensemble models, and a Long Short-Term Memory (LSTM) model to determine the optimal model for emotion detection. Results indicate that the Stacking classifier outperforms other models in accuracy and performance. We also benchmark our models against EmoBERTa, a pre-trained emotion detection model, with our Stacking classifier proving more effective. Finally, the Stacking classifier is deployed via a Streamlit web application, underscoring its potential for real-world applications in text-based emotion analysis.
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