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/]

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

前往荟萃

VIP内容

社交媒体是人们用来创作、分享、交流意见、观点及经验 的网络平台。社交媒体已经涉及到现代人生活的方方面面, 成为信息传播和维系社会关系的重要渠道。而文本是社交 媒体交流的主要载体。

情感分析是一种重要的信息组织方式,研究的目标是自动挖掘 和分析文本中的立场、观点、看法、情绪和喜恶等主观信息。 其一般的研究框架包含情感抽取、分类、检索与归纳等任务。

主要内容

  • 一、情感词向量构建
  • 二、情感分类
  • 三、深层情感分析
  • 四、情感分析应用

作者介绍: 王伟平,博士,主要研究方向:数据库、海量数据处理。分别于1997年、2001年和2006年获得哈尔滨工业大学计算机科学与工程学院学士、硕士和博士学位。2002年7月至9月在香港理工大学访问学习,2005年7月至12月在新加坡国立大学访问学习。2007年6月至今,在高性能计算机研究开发中心工作,任并行数据组项目组长。负责国家自然科学基金青年基金项目、国家信息安全专项项目、国家242信息安全计划项目多项,发表论文20余篇。曾获得2004年度国家科技进步二等奖(排名第9),2008年度计算所优秀员工,2008年入选计算所“百星计划”。

成为VIP会员查看完整内容
18+
0+
更多VIP内容

最新论文

Sentiment Analysis and Emotion Detection in conversation is key in a number of real-world applications, with different applications leveraging different kinds of data to be able to achieve reasonably accurate predictions. Multimodal Emotion Detection and Sentiment Analysis can be particularly useful as applications will be able to use specific subsets of the available modalities, as per their available data, to be able to produce relevant predictions. Current systems dealing with Multimodal functionality fail to leverage and capture the context of the conversation through all modalities, the current speaker and listener(s) in the conversation, and the relevance and relationship between the available modalities through an adequate fusion mechanism. In this paper, we propose a recurrent neural network architecture that attempts to take into account all the mentioned drawbacks, and keeps track of the context of the conversation, interlocutor states, and the emotions conveyed by the speakers in the conversation. Our proposed model out performs the state of the art on two benchmark datasets on a variety of accuracy and regression metrics.

0+
0+
下载
预览
更多最新论文
Top