Aspect Based Sentiment Analysis 基于方面的细粒度情感分析.zip

# ABSA Aspect Based Sentiment Analysis 虽说是基于观点的分析，但也是基于句子层的分析，因为需要按句子进行分析。  ##### 概念参考 - ABSA refer presentation [[ppt](https://www.iaria.org/conferences2016/filesHUSO16/OrpheeDeClercq_Keynote_ABSA.pdf)] - 阿里云的商品评价解析 [[link](https://help.aliyun.com/document_detail/64231.html?spm=5176.12095382.1232858.4.739e3b24xUnvbZ)] | 参数名 | 值 | | -------------- | ------------------------------------------------------------ | | textPolarity | 整条文本情感极性：正、中、负，text字段输入非法时返回-100 | | textIntensity | 整条文本情感程度(取值范围[-1,1]，越大代表越正向，越小代表越负向，接近0代表中性) | | aspectItem | 属性情感列表，每个元素是一个json字段 | | aspectCategory | 属性类别 | | aspectIndex | 属性词所在的起始位置，终结位置 | | aspectTerm | 属性词 | | opinionTerm | 情感词 | | aspectPolarity | 属性片段极性（正、中、负） | ##### Task Process 1. 按句 提取 属性词 2. 按句 提取 情感词 3. 属性词所在起始位置，终止位置 4. 属性词 -> EA分类 5. 情感词 -> 极性分类 6. 整条文本的感情极性（正、负、中） 及其概率值 ##### Done Tasks 根据现有数据集，实际完成的任务 - [x] 按句进行 EA 分类 - [x] 按句进行情感极性分析 ##### To do - [ ] 观点过滤：文字噪音处理、虚假评论、水军、广告、不含观点、无意义文本 - [ ] negation 否定处理 ##### SemEval ABSA - NLP的 SemEval 论文合辑 [[ACL](https://www.aclweb.org/anthology/)] - SemEval - 2014 - ABSA [[competition](http://alt.qcri.org/semeval2014/task4/)] [[data](http://alt.qcri.org/semeval2014/task4/index.php?id=data-and-tools)] - SemEval - 2015 - ABSA [[competition](http://alt.qcri.org/semeval2015/task12/)] [[data](http://alt.qcri.org/semeval2015/task12/index.php?id=data-and-tools)] [[paper](https://www.aclweb.org/anthology/S15-2082)] - SemEval - 2016 - ABSA [[competition](http://alt.qcri.org/semeval2016/task5/)] [[data](http://alt.qcri.org/semeval2016/task5/index.php?id=data-and-tools)] [[guideline](http://alt.qcri.org/semeval2016/task5/data/uploads/absa2016_annotationguidelines.pdf)] [[paper](https://www.aclweb.org/anthology/S16-1002)] - bonus: CodaLab Competitions [[intro](https://www.hse.ru/data/2017/05/31/1171931089/CodaLabCompetitions.pdf)] ##### 可参考的GitHub项目 数据集基本都基于 2014-2016 SemEval 比赛 - [data: self data] [Unsupervised-Aspect-Extraction](https://github.com/ruidan/Unsupervised-Aspect-Extraction) - [data: SemEval-2016] [aspect-extraction](https://github.com/soujanyaporia/aspect-extraction) - [data: SemEval-2015] [AspectBasedSentimentAnalysis](https://github.com/yardstick17/AspectBasedSentimentAnalysis) 跑了下这个项目，其中结合了语法分析和机器学习，按照语法规则抽取的属性词。代码嵌套逻辑比较强，不建议套用。 - [data: SemEval-2016] [Review_aspect_extraction](https://github.com/yafangy/Review_aspect_extraction) - [data: SemEval-2014, 2016] [DE-CNN](https://github.com/howardhsu/DE-CNN) - [data: SemEval-2015] [Coupled-Multi-layer-Attentions](https://github.com/happywwy/Coupled-Multi-layer-Attentions) - [data: SemEval-2016 laptop] [mem_absa](https://github.com/ganeshjawahar/mem_absa) - [data: SemEval-2014] [ABSA-PyTorch](https://github.com/songyouwei/ABSA-PyTorch) - [data: SemEval-2014, 2016] [Attention_Based_LSTM_AspectBased_SA](https://github.com/gangeshwark/Attention_Based_LSTM_AspectBased_SA) - [data: SemEval-2014] [ABSA_Keras](https://github.com/AlexYangLi/ABSA_Keras) 利用了tensorflow hub，适用hub时出现了版本问题未跑通。 - [data: SemEval-2016] [ABSA](https://github.com/LingxB/ABSA/tree/master/Data/SemEval) ##### paper - Deep Learning for Aspect-Based Sentiment Analysis [[paper](https://cs224d.stanford.edu/reports/WangBo.pdf)] - Fine-grained Opinion Mining with Recurrent Neural Networks and Word Embeddings [[paper](https://www.aclweb.org/anthology/D15-1168)] - Encoding Conversation Context for Neural Keyphrase Extraction from Microblog Posts [[paper](https://ai.tencent.com/ailab/media/publications/naacl2018/Encoding_Conversation_Context_for_Neural_Keyphrase_Extraction_from_Microblog_Posts.pdf)] - End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF [[paper](https://arxiv.org/pdf/1603.01354.pdf)] - [2012] 用户评论中的标签抽取以及排序 [[paper](http://lipiji.com/docs/li2011opinion.pdf)] ##### 数据集 ###### 中文 - AI-Challenge [[data](https://drive.google.com/file/d/1OInXRx_OmIJgK3ZdoFZnmqUi0rGfOaQo/view)] - SemEval ABSA 2016 [[data](http://alt.qcri.org/semeval2016/task5/index.php?id=data-and-tools)] ###### 英文 - Amazon product data [[data](http://jmcauley.ucsd.edu/data/amazon/)] - Web data: Amazon reviews [[data](https://snap.stanford.edu/data/web-Amazon.html)] - Amazon Fine Food Reviews [[kaggle](https://www.kaggle.com/snap/amazon-fine-food-reviews)] - SemEval ABSA #### 优化方向 ##### 字/词/句 文本嵌入Embedding ###### 中文 - Chinese Word Vectors [[github](https://github.com/Embedding/Chinese-Word-Vectors)] - nlp_chinese_corpus [[github](https://github.com/brightmart/nlp_chinese_corpus)] - 泛化语料、专业语料、向量化时，如何整合，还是两者独立向量化 ABSA书的目录，可以学习逻辑 #### ABSA Book Outline 1. Introduction 2. Aspect-Based Sentiment Analysis (ABSA) - 2.1. The three tasks of ABSA - 2.2. Domain and benchmark datasets - 2.3. Previous approaches to ABSA tasks - 2.4. Evaluation measures of ABSA tasks 3. Deep Learning for ABSA - 3.1. Multiple layers of DNN - 3.2. Initialization of input vectors - 3.2.1. Word embeddings vectors - 3.2.2. Featuring vectors - 3.2.3. Part-Of-Speech (POS) and chunk tags - 3.2.4. Commonsense knowledge - 3.3. Training process of DNNs - 3.4. Convolutional Neural Network Model (CNN) - 3.4.1. Architecture - 3.4.2. Application in consumer review domain - 3.5. Recurrent Neural Network Models (RNN) - 3.5.1. Computation of RNN models - 3.5.2. Bidirectional RNN - 3.5.3. Attention mechanism and memory networks - 3.5.4. Application in the consumer review domain - 3.5.5. Application in targeted sentiment analysis - 3.6. Recursive Neural Network Model (RecNN) - 3.6.1. Architecture - 3.6.2. Application - 3.7. Hybrid models 4. Comparison of performance on benchmark datasets - 4.1. Opinion target extraction - 4.2. Aspect category detection - 4.3. Sentiment polarity of aspect-based consumer reviews - 4.4. Sentiment polarity of targeted text 5. Challenges - 5.1. Domain adaptation - 5.2. Multilingual application - 5.3. Technical requirements - 5.4. Linguistic complications 6. Conclusion 7. Appendix: List of Abbreviations 8. References