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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文本情感分析是自然语言处理领域的一个重要分支,广泛应用于舆情分析和内容推荐等方面,是近 年来的研究热点。根据使用的不同方法,将其划分为基于情感词典的情感分析方法、基于传统机器学习的情 感分析方法、基于深度学习的情感分析方法。通过对这三种方法进行对比,分析其研究成果,并对不同方法 的优缺点进行归纳总结,介绍相关数据集和评价指标,及应用场景,对情感分析子任务进行简单概括,发现 将来的情感分析问题的研究趋势及应用领域,并为研究者在相关领域方面提供一定的帮助和指导。

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We present skweak, a versatile, Python-based software toolkit enabling NLP developers to apply weak supervision to a wide range of NLP tasks. Weak supervision is an emerging machine learning paradigm based on a simple idea: instead of labelling data points by hand, we use labelling functions derived from domain knowledge to automatically obtain annotations for a given dataset. The resulting labels are then aggregated with a generative model that estimates the accuracy (and possible confusions) of each labelling function. The skweak toolkit makes it easy to implement a large spectrum of labelling functions (such as heuristics, gazetteers, neural models or linguistic constraints) on text data, apply them on a corpus, and aggregate their results in a fully unsupervised fashion. skweak is especially designed to facilitate the use of weak supervision for NLP tasks such as text classification and sequence labelling. We illustrate the use of skweak for NER and sentiment analysis. skweak is released under an open-source license and is available at: https://github.com/NorskRegnesentral/skweak

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