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。

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/]

初步版本,水平有限,有错误或者不完善的地方,欢迎大家提建议和补充,会一直保持更新,本文为专知内容组原创内容,未经允许不得转载,如需转载请发送邮件至fangquanyi@gmail.com 或 联系微信专知小助手(Rancho_Fang)

敬请关注http://www.zhuanzhi.ai 和关注专知公众号,获取第一手AI相关知识

成为VIP会员查看完整内容
Top