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📖 收集NLP领域相关的数据集、论文、开源实现,尤其是情感分析、情绪原因识别、评价对象和评价词抽取方面。
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README.md 添加新的情感原因识别论文和数据集 Dec 5, 2019

README.md

awesome-nlp-sentiment-analysis

收集NLP领域相关的数据集、论文、开源实现,尤其是情感分析、情绪原因识别、评价对象和评价词抽取等方面。

📖 Papers & 🙊 Codes

情感分析

  1. Deep Learning or Sentiment analysis: A Survey. paper

情绪归因

  1. An Ensemble Approach for Emotion Cause Detection with Event Extraction and Multi-Kernel SVMs. paper

  2. Event-Driven Emotion Cause Extraction with Corpus Construction. paper

  3. A Question Answering Approach to Emotion Cause Extraction. paper

  4. A Bootstrap Method for Automatic Rule Acquisition on Emotion Cause Extraction. paper

  5. Emotion Cause Detection for Chinese Micro-Blogs Based on ECOCC Model. paper

  6. Emotion Cause Detection with Linguistic Constructions. paper

  7. Emotion Cause Extraction, A Challenging Task with Corpus Construction. paper

  8. Extracting Causes of Emotions from Text. paper

  9. 基于E-CNN神经网络的情绪原因识别方法. paper

  10. 基于序列标注模型的情绪原因识别方法. paper

  11. 基于文本的情绪自动归因方法研究. paper

  12. A Co-Attention Neural Network Model for Emotion Cause Analysis with Emotional Context Awareness. paper

  13. Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts. paper code

评价对象和评价词抽取

  1. Fine-grained Opinion Mining with Recurrent Neural Networks and Word Embeddings. paper code

  2. Character-based BiLSTM-CRF Incorporating POS and Dictionaries for Chinese Opinion Target Extraction. paper code

  3. A Unified Model for Opinion Target Extraction and Target Sentiment Prediction. paper code

  4. 评价对象抽取研究综述. paper

  5. 使用深度长短时记忆模型对于评价词和评价对象的联合抽取. paper

  6. 基于语义和句法依存特征的评论对象抽取研究. paper

  7. 基于条件随机场的评价对象缺省项识别. paper

  8. 基于CRFs和领域本体的中文微博评价对象抽取研究. paper

  9. 基于微博的情感倾向性分析方法研究. paper

  10. 基于迭代两步CRF模型的评价对象与极性抽取研究. paper

  11. 基于句法特征的评价对象抽取方法研究. paper

  12. 基于层叠CRFs的中文句子评价对象抽取. paper

  13. 评价对象及其倾向性的抽取和判别. paper

  14. 基于非完备信息系统的评价对象情感聚类. paper

  15. 基于CRFs的评价对象抽取特征研究. paper

  16. 基于核心句及句法关系的评价对象抽取. paper

  17. 面向特定领域的产品评价对象自动识别研究. paper

  18. 评价对象抽取及其倾向性分析. paper

  19. Aspect extraction for opinion mining with a deep convolutional neural network. paper

  20. Recursive neural conditional random fields for aspect-based sentiment analysis. paper

  21. Coupled multi-layer attentions for co-extraction of aspect and opinion terms. paper

💾 Dataset

评价对象和评价词抽取

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