/Aspect-Based-Sentiment-Analysis

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

Primary LanguagePython

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]
  • 阿里云的商品评价解析 [link]
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

  • 按句进行 EA 分类
  • 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
可参考的GitHub项目

数据集基本都基于 2014-2016 SemEval 比赛

paper
  • Deep Learning for Aspect-Based Sentiment Analysis [paper]
  • Fine-grained Opinion Mining with Recurrent Neural Networks and Word Embeddings [paper]
  • Encoding Conversation Context for Neural Keyphrase Extraction from Microblog Posts [paper]
  • End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF [paper]
  • [2012] 用户评论中的标签抽取以及排序 [paper]
数据集
中文
  • AI-Challenge [data]
  • SemEval ABSA 2016 [data]
英文
  • Amazon product data [data]
  • Web data: Amazon reviews [data]
  • Amazon Fine Food Reviews [kaggle]
  • SemEval ABSA

优化方向

字/词/句 文本嵌入Embedding
中文
  • Chinese Word Vectors [github]
  • nlp_chinese_corpus [github]
  • 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