实例介绍
【实例简介】
Bing Liu的新书，<Sentiment Analysis and Opinion Mining>很好的一本情感分析和意见挖掘书籍，Liu在阅读大量论文的基础上写的，涵盖的行业的发展。
SYNTHESIS LECTURES ON HUMAN LANGUAGE TECHNOLOGIES ii Introduction to Arabic Natural Language Processing Nizar y habash Cross-Language Information Retrieval lan-Yun nie 2010 Automated Grammatical Error Detection for Language learners Claudia leacock, Martin Chodorow, Michael Gamon, Joel Tetreault 2010 Data-Intensive Text Processing with MapReduce Jimmy Lin, Chris Dyer 2010 Semantic Role Labeling Martha palmer. Daniel Gildea, Nianwen Xue 2010 poken Dialogue systems Kristina Jokinen, Michael McTear 2009 Introduction to Chinese Natural Language Processing Kam-Fai Wong, Wenjie Li, Ruifeng Xu, Zheng-sheng Zhang 2009 Introduction to Linguistic Annotation and Text Analytics Graham wilcock 2009 Dependency Parsing Sandra Kubler, Ryan McDonald, Joakim Nivre 2009 Statistical Language Models for Information Retrieval Cheng Xiang zhai 2008 Copyright c 2012 by morgan claypool All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means- electronic, mechanical, photocopy, recording, or any other except for brief quotations in printed reviews, without the prior permission of the publisher. Sentiment analysis and Opinion mining Bing li www.morganclaypool.com ISBN: 9781608458844 paperback ISBN:9781608458851 ebook DOⅠ:10.2200/S00416ED1V01Y201204HLT016 A Publication in the SYNTHESIS LECTURES ON HUMAN LANGUAGE TECHNOLOGIES Lecture #16 Series Editor: Graeme Hirst, University of Toronto Series Issn ISsn 1947-4040 print Issn 1947-4059 electronic Sentiment analysis and Opinion mining Bing University of Ilinois at Chicago SYNTHESIS LECTURES ON HUMAN LANGUAGE TECHNOLOGIES #16 MORGAN&CLAYPOOL PUBLISHERS ABSTRaCT Sentiment analysis and opinion mining is the field of study that analyzes peoples opinions, senti ments, evaluations, attitudes, and emotions from written language. It is one of the most active re search areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews,forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how oth ers see and evaluate the world. For this reason when we need to make a decision we often seek out the opinions of others This is true not only for individuals but also for organizations This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, research- ers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining Lecture slides are also available online KEYWORDS sentiment analysis, opinion mining, emotion, affect, evaluation, attitude, mood, social media natural language progressing, text mining Acknowledgments I would like to thank my former and current students--Zhiyuan Chen, Xiaowen Ding, Geli Fei, Murthy Ganapathibhotla, Minqing Hu, Nitin Jindal, Huayi Li, Arjun Mukherjee, Quang Qiu (visiting student from Zhejiang University), William Underwood, Andrea Vaccari, Zhongwu Zhai (visiting student from Tsinghua University), and Lei zhang-for contributing numerous research ideas over the years. Discussions with many researchers also helped shape the book: Malu g Castellanos, Dennis Chong, Umesh Dayal, Eduard Dragut, Riddhiman Ghosh, Natalie Glance Meichun Hsu, Jing Jiang, Birgit Konig, Xiaoli Li, Tieyun Qian, Gang Xu, Philip S. Yu, Clement Yu, and Cheng Xiang Zhai. I am also very grateful to two anonymous reviewers. Despite their busy schedules, they read the book very carefully and gave me many excellent suggestions. I have taken each and every one of them into consideration while improving the book On the publication side, I thank the Editor, Dr Graeme Hirst, and the President and ceo of morgan claypool Publishers Mr Michael Morgan, who have managed to get everything done on time and provided me with many pieces of valuable advice. Finally, my greatest gratitude goes to my own family: Yue, Shelley and Kate, who have helped in so many ways Contents Preface 1. Sentiment Analysis: A Fascinating Problem 1.1 Sentiment Analysis applications 2 1.2 Sentiment Analysis Research 1.2. 1 Different Levels of analysis 4 1.2.2 Sentiment lexicon and Its issues 1.2.3 Natural Language Processing Issues 1.3 Opinion Spam Detection 1.4 What's ahead 7 2. The Problem of Sentiment analysis..................................9 2.1 Problem Definitions 10 211 Opinion Defintion……… 10 2.1.2 Sentiment Analysis Tasks 14 2.2 Opinion Summarization..........................17 2. 3 Different Types of Opinions..............18 2.3.1 Regular and Comparative Opinions 18 23.2 Explicit and Implicit Opinions…… 19 2.4 Subjectivity and Emotion ..... 2.5 Author and Reader Standpoint............................... 21 2.6 Summary …21 3. Document sentiment classification 23 3.1 Sentiment Classification USing Supervised Learning 24 3. 2 Sentiment Classification Using Unsupervised Learning .............28 3.3 Sentiment Rating Prediction 30 3.4 Cross-Domain sentiment Classification 31 X SENTIMENT ANALYSIS AND OPINION MINING 3.5 Cross-Language Sentiment Classification 34 3.6 Summary........136 4. Sentence Subjectivity and Sentiment Classification.................37 4. 1 Subjectivity classification 38 4.2 Sentence Sentiment classification 41 4.3 Dealing with Conditional Sentences 43 4.4 Dealing with Sarcastic Sentences……… 44 4.5 Cross-Language subjectivity and Sentiment Classification...........45 4.6 USing Discourse Information for Sentiment Classification 4.7 Summary… 47 5. Aspect-Based Sentiment Analysis 5.1 Aspect Sentiment Classification 5.2 Basic rules of opinions and Compositional semantics…………….53 5.3 Aspect Extraction 5.3. 1 Finding Frequent Nouns and Noun Phrases..............59 5.3.2 USing Opinion and Target relations .61 5.3.3 Using Supervised learning .62 5.3.4 USing Topic Models 音音 5.3.5 Mapping Implicit Aspects………… 5.4 Identifying Resource Usage Aspect…… .67 5.5 Simutaneous Opinion Lexicon Expansion and Aspect Extraction 5.6 Grouping Aspects into Categories 5.7 Entity Opinion Holder, and Time Extraction 5.8 Coreference Resolution and Word Sense Disambiguation...........75 5.9 Summary…… 76 Sentiment Lexicon generation 6.1 Dictionary-Based Approach 80 6.2 Corpus-Based A pproacn……… 6.3 Desirable and Undesirable facts 87 6.4 Summary 88 7. Opinion Summarization.…… 91 71 Aspect- Based Opinion Summarization……91 7.2 Improvements to Aspect-Based Opinion Summarization 94 【实例截图】
【核心代码】
Bing Liu的新书，<Sentiment Analysis and Opinion Mining>很好的一本情感分析和意见挖掘书籍，Liu在阅读大量论文的基础上写的，涵盖的行业的发展。
SYNTHESIS LECTURES ON HUMAN LANGUAGE TECHNOLOGIES ii Introduction to Arabic Natural Language Processing Nizar y habash Cross-Language Information Retrieval lan-Yun nie 2010 Automated Grammatical Error Detection for Language learners Claudia leacock, Martin Chodorow, Michael Gamon, Joel Tetreault 2010 Data-Intensive Text Processing with MapReduce Jimmy Lin, Chris Dyer 2010 Semantic Role Labeling Martha palmer. Daniel Gildea, Nianwen Xue 2010 poken Dialogue systems Kristina Jokinen, Michael McTear 2009 Introduction to Chinese Natural Language Processing Kam-Fai Wong, Wenjie Li, Ruifeng Xu, Zheng-sheng Zhang 2009 Introduction to Linguistic Annotation and Text Analytics Graham wilcock 2009 Dependency Parsing Sandra Kubler, Ryan McDonald, Joakim Nivre 2009 Statistical Language Models for Information Retrieval Cheng Xiang zhai 2008 Copyright c 2012 by morgan claypool All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means- electronic, mechanical, photocopy, recording, or any other except for brief quotations in printed reviews, without the prior permission of the publisher. Sentiment analysis and Opinion mining Bing li www.morganclaypool.com ISBN: 9781608458844 paperback ISBN:9781608458851 ebook DOⅠ:10.2200/S00416ED1V01Y201204HLT016 A Publication in the SYNTHESIS LECTURES ON HUMAN LANGUAGE TECHNOLOGIES Lecture #16 Series Editor: Graeme Hirst, University of Toronto Series Issn ISsn 1947-4040 print Issn 1947-4059 electronic Sentiment analysis and Opinion mining Bing University of Ilinois at Chicago SYNTHESIS LECTURES ON HUMAN LANGUAGE TECHNOLOGIES #16 MORGAN&CLAYPOOL PUBLISHERS ABSTRaCT Sentiment analysis and opinion mining is the field of study that analyzes peoples opinions, senti ments, evaluations, attitudes, and emotions from written language. It is one of the most active re search areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews,forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how oth ers see and evaluate the world. For this reason when we need to make a decision we often seek out the opinions of others This is true not only for individuals but also for organizations This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, research- ers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining Lecture slides are also available online KEYWORDS sentiment analysis, opinion mining, emotion, affect, evaluation, attitude, mood, social media natural language progressing, text mining Acknowledgments I would like to thank my former and current students--Zhiyuan Chen, Xiaowen Ding, Geli Fei, Murthy Ganapathibhotla, Minqing Hu, Nitin Jindal, Huayi Li, Arjun Mukherjee, Quang Qiu (visiting student from Zhejiang University), William Underwood, Andrea Vaccari, Zhongwu Zhai (visiting student from Tsinghua University), and Lei zhang-for contributing numerous research ideas over the years. Discussions with many researchers also helped shape the book: Malu g Castellanos, Dennis Chong, Umesh Dayal, Eduard Dragut, Riddhiman Ghosh, Natalie Glance Meichun Hsu, Jing Jiang, Birgit Konig, Xiaoli Li, Tieyun Qian, Gang Xu, Philip S. Yu, Clement Yu, and Cheng Xiang Zhai. I am also very grateful to two anonymous reviewers. Despite their busy schedules, they read the book very carefully and gave me many excellent suggestions. I have taken each and every one of them into consideration while improving the book On the publication side, I thank the Editor, Dr Graeme Hirst, and the President and ceo of morgan claypool Publishers Mr Michael Morgan, who have managed to get everything done on time and provided me with many pieces of valuable advice. Finally, my greatest gratitude goes to my own family: Yue, Shelley and Kate, who have helped in so many ways Contents Preface 1. Sentiment Analysis: A Fascinating Problem 1.1 Sentiment Analysis applications 2 1.2 Sentiment Analysis Research 1.2. 1 Different Levels of analysis 4 1.2.2 Sentiment lexicon and Its issues 1.2.3 Natural Language Processing Issues 1.3 Opinion Spam Detection 1.4 What's ahead 7 2. The Problem of Sentiment analysis..................................9 2.1 Problem Definitions 10 211 Opinion Defintion……… 10 2.1.2 Sentiment Analysis Tasks 14 2.2 Opinion Summarization..........................17 2. 3 Different Types of Opinions..............18 2.3.1 Regular and Comparative Opinions 18 23.2 Explicit and Implicit Opinions…… 19 2.4 Subjectivity and Emotion ..... 2.5 Author and Reader Standpoint............................... 21 2.6 Summary …21 3. Document sentiment classification 23 3.1 Sentiment Classification USing Supervised Learning 24 3. 2 Sentiment Classification Using Unsupervised Learning .............28 3.3 Sentiment Rating Prediction 30 3.4 Cross-Domain sentiment Classification 31 X SENTIMENT ANALYSIS AND OPINION MINING 3.5 Cross-Language Sentiment Classification 34 3.6 Summary........136 4. Sentence Subjectivity and Sentiment Classification.................37 4. 1 Subjectivity classification 38 4.2 Sentence Sentiment classification 41 4.3 Dealing with Conditional Sentences 43 4.4 Dealing with Sarcastic Sentences……… 44 4.5 Cross-Language subjectivity and Sentiment Classification...........45 4.6 USing Discourse Information for Sentiment Classification 4.7 Summary… 47 5. Aspect-Based Sentiment Analysis 5.1 Aspect Sentiment Classification 5.2 Basic rules of opinions and Compositional semantics…………….53 5.3 Aspect Extraction 5.3. 1 Finding Frequent Nouns and Noun Phrases..............59 5.3.2 USing Opinion and Target relations .61 5.3.3 Using Supervised learning .62 5.3.4 USing Topic Models 音音 5.3.5 Mapping Implicit Aspects………… 5.4 Identifying Resource Usage Aspect…… .67 5.5 Simutaneous Opinion Lexicon Expansion and Aspect Extraction 5.6 Grouping Aspects into Categories 5.7 Entity Opinion Holder, and Time Extraction 5.8 Coreference Resolution and Word Sense Disambiguation...........75 5.9 Summary…… 76 Sentiment Lexicon generation 6.1 Dictionary-Based Approach 80 6.2 Corpus-Based A pproacn……… 6.3 Desirable and Undesirable facts 87 6.4 Summary 88 7. Opinion Summarization.…… 91 71 Aspect- Based Opinion Summarization……91 7.2 Improvements to Aspect-Based Opinion Summarization 94 【实例截图】
【核心代码】
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