狭义的情感分析(sentiment analysis)是指利用计算机实现对文本数据的观点、情感、态度、情绪等的分析挖掘。广义的情感分析则包括对图像视频、语音、文本等多模态信息的情感计算。简单地讲,情感分析研究的目标是建立一个有效的分析方法、模型和系统,对输入信息中某个对象分析其持有的情感信息,例如观点倾向、态度、主观观点或喜怒哀乐等情绪表达。

知识荟萃

情感分析 ( Sentiment Analysis ) 专知荟萃

入门学习

  1. 斯坦福大学自然语言处理第七课“情感分析(Sentiment Analysis)” [http://52opencourse.com/235/%E6%96%AF%E5%9D%A6%E7%A6%8F%E5%A4%A7%E5%AD%A6%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86%E7%AC%AC%E4%B8%83%E8%AF%BE-%E6%83%85%E6%84%9F%E5%88%86%E6%9E%90%EF%BC%88sentiment-analysis%EF%BC%89] [https://class.coursera.org/nlp/]
  2. 情感分类方法简介 [http://www.jianshu.com/p/61212b11769a]
  3. NLP 笔记 - Sentiment Analysis [http://www.shuang0420.com/2017/06/01/NLP%20%E7%AC%94%E8%AE%B0%20-%20Sentiment%20Analysis/]
  4. 斯坦福CoreNLP —— 用Java给Twitter进行情感分析 [http://zqdevres.qiniucdn.com/data/20131225114906/index.html]
  5. TEXT CLASSIFICATION FOR SENTIMENT ANALYSIS – NLTK + SCIKIT-LEARN [https://streamhacker.com/2012/11/22/text-classification-sentiment-analysis-nltk-scikitlearn/]
  6. Sentiment Analysis in Python [http://andybromberg.com/sentiment-analysis-python/]
  7. Basic Sentiment Analysis with Python [http://fjavieralba.com/basic-sentiment-analysis-with-python.html]
  8. 中文情感分析 (Sentiment Analysis) 的难点在哪?现在做得比较好的有哪几家? [https://www.zhihu.com/question/20700012]

综述

  1. Sentiment analysis and opinion mining https://www.cs.uic.edu/~liub/FBS/SentimentAnalysis-and-OpinionMining.pdf~
  2. Sentiment analysis algorithms and applications: A survey [https://pan.baidu.com/s/1miR4DD2] http://www.sciencedirect.com/science/article/pii/S2090447914000550
  3. Sentiment Analysis:A Comparative Study On Different Approaches https://www.researchgate.net/profile/Amal_Ganesh/publication/303848210_Sentiment_Analysis_A_Comparative_Study_on_Different_Approaches/links/576a633208aeb526b69b84d7/Sentiment-Analysis-A-Comparative-Study-on-Different-Approaches.pdf
  4. 文本情感分析 [http://jos.org.cn/ch/reader/create_pdf.aspx?file_no=3832&journal_id=jos]
  5. Opinion Mining and Sentiment Analysis Bo Pang1 and Lillian Lee2 [https://www.cse.iitb.ac.in/~pb/cs626-449-2009/prev-years-other-things-nlp/sentiment-analysis-opinion-mining-pang-lee-omsa-published.pdf]

进阶论文

2002

  1. Bo Pang, Lillian Lee, Shivakumar Vaithyanathan. Thumbs up? Sentiment Classification using Machine Learning Techniques. EMNLP, 2002.
    [https://wenku.baidu.com/view/efa9391d650e52ea551898e8.html]

2004

  1. Minqing Hu and Bing Liu. Mining and summarizing customer reviews. KDD: 168-177, 2004.
    [https://dl.acm.org/citation.cfm?id=1014073]

2011

  1. Maite Taboada, Julian Brooke, Milan Tofiloski, Kimberly Voll, and Manfred Stede. Lexicon-Based Methods for Sentiment Analysis. Computational Linguistics: 37(2), 267-307. 2011.
    [https://dl.acm.org/citation.cfm?id=1014073]
  2. Dmitriy Bespalov, Bing Bai, Yanjun Qi, Ali Shokoufandeh. Sentiment Classification Based on Supervised Latent n-gram Analysis. Proceedings of the Conference on Information and Knowledge Management, 2011.
    [https://dl.acm.org/citation.cfm?id=2063576.2063635]

2012

  1. Bing Liu. 2012. Sentiment analysis and opinion mining. In Synthesis lectures on human language technologies, 1-167.
    [http://download.csdn.net/download/kevin_done_register/6750185]

2014

  1. Simpler is better? Lexicon-based ensemble sentiment classification beats supervised methods.
    [https://www.cs.rpi.edu/szymansk/papers/C3-ASONAM14.pdf]
  2. Duyu Tang, Furu Wei, Bing Qin, Ting Liu, Ming Zhou. 2014. Building Large-Scale Twitter-Specific Sentiment Lexicon : A Representation Learning Approach. International Conference on Computational Linguistics(COLING).
    [http://www.aclweb.org/anthology/C14-1018]

2015

  1. Sentiment Analysis: mining sentiments, opinions, and emotions 图书
    [https://www.cs.uic.edu/~liub/FBS/sentiment-opinion-emotion-analysis.html]
  2. Rie Johnson and Tong Zhang. Effective use of word order for text categorization with convolutional neural networks. In NAACL 2015.
    [https://arxiv.org/abs/1412.1058]
  3. Rie Johnson, and Tong Zhang. Semi-supervised convolutional neural networks for text categorization via region embedding. In NIPS 2015.
    [http://pubmedcentralcanada.ca/pmcc/articles/PMC4831869/]
  4. Xiang Zhang, Junbo Zhao, and Yann LeCun. Character-level convolutional networks for text classification. In NIPS 2015.
    [http://arxiv.org/abs/1509.01626]

2016

  1. Comprehensive Study on Lexicon-based Ensemble Classification Sentiment Analysis.
    [http://www.mdpi.com/1099-4300/18/1/4]
  2. Duyu Tang, Furu Wei, Bing Qin, Nan Yang, Ting Liu, Ming Zhou. 2016. Sentiment Embeddings with Applications to Sentiment Analysis. IEEE Transactions on Knowledge and Data Engineering (TKDE).
    [https://www.mendeley.com/research-papers/sentiment-embeddings-applications-sentiment-analysis/]
  3. Yafeng Ren, Yue Zhang, Meishan Zhang, and Donghong Ji. 2016. Improving Twitter Sentiment Classification Using Topic-Enriched Multi-Prototype Word Embeddings. In Proceedings of AAAI.
    [https://aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/11925]
  4. Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, Eduard Hovy. 2016. Hierarchical Attention Networks for Document Classification. In NAACL 2016.
    [https://www.microsoft.com/en-us/research/publication/hierarchical-attention-networks-document-classification/]
  5. Rie Johnson, and Tong Zhang. Supervised and Semi-Supervised Text Categorization using LSTM for Region Embeddings. In ICML 2016.
    [https://arxiv.org/abs/1602.02373]
  6. Alexis Conneau, Holger Schwenk, Loïc Barrault, and Yann Lecun. 2016. Very Deep Convolutional Networks for Natural Language Processing. arXiv.org 1606.01781.
    [https://arxiv.org/abs/1606.01781v1]
  7. Huimin Chen, Maosong Sun, Cunchao Tu, Yankai Lin, Zhiyuan Liu. Neural Sentiment Classification with User and Product Attention. In EMNLP 2016.
    [http://nlp.csai.tsinghua.edu.cn/~lzy/publications/emnlp2016.pdf]
  8. Lin Gui, Dongyin Wu, Ruifeng Xu*, Qin Lu, Yu Zhou. Event-Driven Emotion Cause Extraction with Corpus Construction. In EMNLP 2016.
    [http://pdfs.semanticscholar.org/120b/d71c72f9477dec6b5291c32f73ae4afbf163.pdf]

Tutorial

  1. 面向社会媒体的文本情感分析 秦兵 哈尔滨工业大学 2017.9.16 北京 第六届全国社会媒体处理大会 [https://pan.baidu.com/s/1i5qxd1V]
  2. 文本情绪分类关键技术研究 李寿山 苏州大学,自然语言处理实验室 2017.9.16 北京 第六届全国社会媒体处理大会 [https://pan.baidu.com/s/1pLLsV3d]
  3. Affective Computing on Social Media Data 贾珈 - 清华大学 2017.9.16 北京 第六届全国社会媒体处理大会 [https://pan.baidu.com/s/1mhDHrxY]
  4. Sentiment Analysis with Neural Network 唐都钰、张梅山  深度学习与情感分析 2016 [https://pan.baidu.com/s/1c2NHlNM] [https://pan.baidu.com/s/1c2ETG0S]
  5. A Short Overview On Sentiment Analysis 黄民烈 清华大学 2016 [https://pan.baidu.com/s/1o7XVV0u]
  6. LingPipe Sentiment 一个java自然语言处理包 [http://alias-i.com/lingpipe/demos/tutorial/sentiment/read-me.html]

代码

  1. Sentiment TreeBank 斯坦福结构依存情感分析演示 [http://nlp.stanford.edu:8080/sentiment/rntnDemo.html]
  2. Sentiment Analysis with Python NLTK Text Classification [http://text-processing.com/demo/sentiment/]
  3. Vivekn's sentiment model [https://github.com/vivekn/sentiment/]
  4. nltk -sentiment analysis tool, Lexical, Dictionary-based, Rule-based. [http://www.nltk.org/]
  5. twitter-sent-dnn Supervised Machine Learning, Deep Learning, Convolutional Neural Network. [https://github.com/xiaohan2012/twitter-sent-dnn]

视频教程

  1. 斯坦福大学自然语言处理第七课-情感分析 [https://class.coursera.org/nlp/]

数据集

  1. Stanford Sentiment Treebank [https://nlp.stanford.edu/sentiment/code.html]
  2. Amazon product dataset  [http://jmcauley.ucsd.edu/data/amazon/]
  3. IMDB movies reviews dataset [http://ai.stanford.edu/~amaas/data/sentiment/]
  4. Sentiment Labelled Sentences Data Set  [https://archive.ics.uci.edu/ml/datasets/Sentiment+Labelled+Sentences]

领域专家

  1. 黄民烈 [http://www.tsinghua.edu.cn/publish/cs/4616/2013/20131122151220708543803/20131122151220708543803_.html]
  2. 李寿山 [http://nlp.suda.edu.cn/~lishoushan/]
  3. Bing Liu [https://www.cs.uic.edu/~liub/]
  4. John Blitzer  [http://john.blitzer.com/]
  5. 万小军 [https://sites.google.com/site/wanxiaojun1979/]
  6. 唐都钰 哈尔滨工业大学 [https://www.microsoft.com/en-us/research/people/dutang/]

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主题: Jointly Modeling Aspect and Sentiment with Dynamic Heterogeneous Graph Neural Networks

摘要: 基于目标的情感分析(TBSA)旨在检测意见方面(方面提取)和针对他们的情感极性(情感检测)。先前的管道和集成方法都无法精确地建模这两个目标之间的固有联系。在本文中,我们提出了一种新颖的动态异构图,以显式方式对两个目标进行联合建模。普通单词和情感标签都被视为异质图中的节点,以便方面单词可以与情感信息进行交互。该图使用多种类型的依赖项进行初始化,并在实时预测期间进行动态修改。在基准数据集上进行的实验表明,我们的模型优于最新模型。进一步的分析表明,在多意见方面和无意见方面的情况下,我们的模型在具有挑战性的实例上均获得了显着的性能提升。

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The purpose of the study is to investigate the relative effectiveness of four different sentiment analysis techniques: (1) unsupervised lexicon-based model using Sent WordNet; (2) traditional supervised machine learning model using logistic regression; (3) supervised deep learning model using Long Short-Term Memory (LSTM); and, (4) advanced supervised deep learning models using Bidirectional Encoder Representations from Transformers (BERT). We use publicly available labeled corpora of 50,000 movie reviews originally posted on internet movie database (IMDB) for analysis using Sent WordNet lexicon, logistic regression, LSTM, and BERT. The first three models were run on CPU based system whereas BERT was run on GPU based system. The sentiment classification performance was evaluated based on accuracy, precision, recall, and F1 score. The study puts forth two key insights: (1) relative efficacy of four highly advanced and widely used sentiment analysis techniques; (2) undisputed superiority of pre-trained advanced supervised deep learning BERT model in sentiment analysis from text data. This study provides professionals in analytics industry and academicians working on text analysis key insight regarding comparative classification performance evaluation of key sentiment analysis techniques, including the recently developed BERT. This is the first research endeavor to compare the advanced pre-trained supervised deep learning model of BERT vis-\`a-vis other sentiment analysis models of LSTM, logistic regression, and Sent WordNet.

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The purpose of the study is to investigate the relative effectiveness of four different sentiment analysis techniques: (1) unsupervised lexicon-based model using Sent WordNet; (2) traditional supervised machine learning model using logistic regression; (3) supervised deep learning model using Long Short-Term Memory (LSTM); and, (4) advanced supervised deep learning models using Bidirectional Encoder Representations from Transformers (BERT). We use publicly available labeled corpora of 50,000 movie reviews originally posted on internet movie database (IMDB) for analysis using Sent WordNet lexicon, logistic regression, LSTM, and BERT. The first three models were run on CPU based system whereas BERT was run on GPU based system. The sentiment classification performance was evaluated based on accuracy, precision, recall, and F1 score. The study puts forth two key insights: (1) relative efficacy of four highly advanced and widely used sentiment analysis techniques; (2) undisputed superiority of pre-trained advanced supervised deep learning BERT model in sentiment analysis from text data. This study provides professionals in analytics industry and academicians working on text analysis key insight regarding comparative classification performance evaluation of key sentiment analysis techniques, including the recently developed BERT. This is the first research endeavor to compare the advanced pre-trained supervised deep learning model of BERT vis-\`a-vis other sentiment analysis models of LSTM, logistic regression, and Sent WordNet.

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