点击上方“专知”关注获取专业AI知识!
【导读】主题荟萃知识是专知的核心功能之一,为用户提供AI领域系统性的知识学习服务。主题荟萃为用户提供全网关于该主题的精华(Awesome)知识资料收录整理,使得AI从业者便捷学习和解决工作问题!在专知人工智能主题知识树基础上,主题荟萃由专业人工编辑和算法工具辅助协作完成,并保持动态更新!另外欢迎对此创作主题荟萃感兴趣的同学,请加入我们专知AI创作者计划,共创共赢! 今天专知为大家呈送第十七篇专知主题荟萃-情感分析知识资料大全集荟萃 (入门/进阶/综述/视频/代码/专家等),请大家查看!专知访问www.zhuanzhi.ai, 或关注微信公众号后台回复" 专知"进入专知,搜索主题“情感分析”查看。此外,我们也提供该文网页桌面手机端(www.zhuanzhi.ai)完整访问,可直接点击访问收录链接地址,以及pdf版下载链接,请文章末尾查看!此为初始版本,请大家指正补充,欢迎在后台留言!欢迎大家分享转发~
情感分析 ( Sentiment Analysis ) 专知荟萃
入门学习
进阶论文
Tutorial
综述
代码
视频教程
领域专家
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/]
情感分类方法简介 [http://www.jianshu.com/p/61212b11769a]
NLP 笔记 - Sentiment Analysis [http://www.shuang0420.com/2017/06/01/NLP%20%E7%AC%94%E8%AE%B0%20-%20Sentiment%20Analysis/]
斯坦福CoreNLP —— 用Java给Twitter进行情感分析 [http://zqdevres.qiniucdn.com/data/20131225114906/index.html]
TEXT CLASSIFICATION FOR SENTIMENT ANALYSIS – NLTK + SCIKIT-LEARN [https://streamhacker.com/2012/11/22/text-classification-sentiment-analysis-nltk-scikitlearn/]
Sentiment Analysis in Python [http://andybromberg.com/sentiment-analysis-python/]
Basic Sentiment Analysis with Python [http://fjavieralba.com/basic-sentiment-analysis-with-python.html]
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]
2002
Bo Pang, Lillian Lee, Shivakumar Vaithyanathan. Thumbs up? Sentiment Classification using Machine Learning Techniques. EMNLP, 2002.
[https://wenku.baidu.com/view/efa9391d650e52ea551898e8.html]
2004
Minqing Hu and Bing Liu. Mining and summarizing customer reviews. KDD: 168-177, 2004.
[https://dl.acm.org/citation.cfm?id=1014073]
2011
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]
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
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
Simpler is better? Lexicon-based ensemble sentiment classification beats supervised methods.
[https://www.cs.rpi.edu/szymansk/papers/C3-ASONAM14.pdf]
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
Sentiment Analysis: mining sentiments, opinions, and emotions 图书
[https://www.cs.uic.edu/~liub/FBS/sentiment-opinion-emotion-analysis.html\]
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]
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/]
Xiang Zhang, Junbo Zhao, and Yann LeCun. Character-level convolutional networks for text classification. In NIPS 2015.
[http://arxiv.org/abs/1509.01626]
2016
Comprehensive Study on Lexicon-based Ensemble Classification Sentiment Analysis.
[http://www.mdpi.com/1099-4300/18/1/4]
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/]
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]
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/]
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]
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]
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]
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]
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]
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]
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]
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]
A Short Overview On Sentiment Analysis 黄民烈 清华大学 2016 [https://pan.baidu.com/s/1o7XVV0u]
LingPipe Sentiment 一个java自然语言处理包 [http://alias-i.com/lingpipe/demos/tutorial/sentiment/read-me.html]
Sentiment analysis and opinion mininghttps://www.cs.uic.edu/~liub/FBS/SentimentAnalysis-and-OpinionMining.pdf~
Sentiment analysis algorithms and applications: A survey [https://pan.baidu.com/s/1miR4DD2]http://www.sciencedirect.com/science/article/pii/S2090447914000550
Sentiment Analysis:A Comparative Study On Different Approacheshttps://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
文本情感分析 [http://jos.org.cn/ch/reader/create_pdf.aspx?file_no=3832&journal_id=jos\]
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\]
Sentiment TreeBank 斯坦福结构依存情感分析演示 [http://nlp.stanford.edu:8080/sentiment/rntnDemo.html]
Sentiment Analysis with Python NLTK Text Classification [http://text-processing.com/demo/sentiment/]
Vivekn's sentiment model [https://github.com/vivekn/sentiment/]
nltk -sentiment analysis tool, Lexical, Dictionary-based, Rule-based. [http://www.nltk.org/]
twitter-sent-dnn Supervised Machine Learning, Deep Learning, Convolutional Neural Network. [https://github.com/xiaohan2012/twitter-sent-dnn]
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
Stanford Sentiment Treebank [https://nlp.stanford.edu/sentiment/code.html]
Amazon product dataset [http://jmcauley.ucsd.edu/data/amazon/]
IMDB movies reviews dataset [http://ai.stanford.edu/~amaas/data/sentiment/\]
Sentiment Labelled Sentences Data Set [https://archive.ics.uci.edu/ml/datasets/Sentiment+Labelled+Sentences]
黄民烈 [http://www.tsinghua.edu.cn/publish/cs/4616/2013/20131122151220708543803/20131122151220708543803_.html\]
李寿山 [http://nlp.suda.edu.cn/~lishoushan/\]
Bing Liu [https://www.cs.uic.edu/~liub/\]
John Blitzer [http://john.blitzer.com/]
万小军 [https://sites.google.com/site/wanxiaojun1979/]
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(到专知网站www.zhuanzhi.ai 主题下评论),会一直保持更新,敬请关注http://www.zhuanzhi.ai 和关注专知公众号,获取最新AI相关知识。
特别提示-专知情感分析主题:
获取完整版查看,最新更新知识资料,请PC登录www.zhuanzhi.ai或者点击阅读原文,注册登录,顶端搜索“情感分析” 主题,查看获得专知荟萃全集知识等资料,直接PC端访问体验更佳!如下图所示~
此外,请关注专知公众号(扫一扫最下面专知二维码,或者点击上方蓝色专知),
后台回复“情感分析”或者“SA” 就可以在手机端获取专知情感分析资料查看链接地址,直接打开荟萃资料的链接地址~~
请扫描专知小助手,加入专知人工智能群,交流分享~
往期专知荟萃知识资料全集获取(关注本公众号-专知,获取下载链接),请查看:
【专知荟萃01】深度学习知识资料大全集(入门/进阶/论文/代码/数据/综述/领域专家等)(附pdf下载)
【专知荟萃02】自然语言处理NLP知识资料大全集(入门/进阶/论文/Toolkit/数据/综述/专家等)(附pdf下载)
【专知荟萃03】知识图谱KG知识资料全集(入门/进阶/论文/代码/数据/综述/专家等)(附pdf下载)
【专知荟萃04】自动问答QA知识资料全集(入门/进阶/论文/代码/数据/综述/专家等)(附pdf下载)
【专知荟萃05】聊天机器人Chatbot知识资料全集(入门/进阶/论文/软件/数据/专家等)(附pdf下载)
【专知荟萃06】计算机视觉CV知识资料大全集(入门/进阶/论文/课程/会议/专家等)(附pdf下载)
【专知荟萃07】自动文摘AS知识资料全集(入门/进阶/代码/数据/专家等)(附pdf下载)
【专知荟萃08】图像描述生成Image Caption知识资料全集(入门/进阶/论文/综述/视频/专家等)
【专知荟萃09】目标检测知识资料全集(入门/进阶/论文/综述/视频/代码等)
【专知荟萃10】推荐系统RS知识资料全集(入门/进阶/论文/综述/视频/代码等)
【专知荟萃11】GAN生成式对抗网络知识资料全集(理论/报告/教程/综述/代码等)
【专知荟萃12】信息检索 Information Retrieval 知识资料全集(入门/进阶/综述/代码/专家,附PDF下载)
【专知荟萃13】工业学术界用户画像 User Profile 实用知识资料全集(入门/进阶/竞赛/论文/PPT,附PDF下载)
【专知荟萃14】机器翻译 Machine Translation知识资料全集(入门/进阶/综述/视频/代码/专家,附PDF下载)
【专知荟萃15】图像检索Image Retrieval知识资料全集(入门/进阶/综述/视频/代码/专家,附PDF下载)
【专知荟萃16】主题模型Topic Model知识资料全集(基础/进阶/论文/综述/代码/专家,附PDF下载)
-END-
欢迎使用专知
专知,一个新的认知方式!专注在人工智能领域为AI从业者提供专业可信的知识分发服务, 包括主题定制、主题链路、搜索发现等服务,帮你又好又快找到所需知识。
使用方法>>访问www.zhuanzhi.ai, 或点击文章下方“阅读原文”即可访问专知
中国科学院自动化研究所专知团队
@2017 专知
专 · 知
关注我们的公众号,获取最新关于专知以及人工智能的资讯、技术、算法、深度干货等内容。扫一扫下方关注我们的微信公众号。
点击“阅读原文”,使用专知!