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Bing Liu - Sentiment Analysis and Opinion Mining

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Bing Liu Sentiment Analysis and Opinion Mining
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Sentiment analysis and opinion mining is the field of study that analyzes peoples opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research 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 others 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, researchers 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.
Table of Contents: Preface / Sentiment Analysis: A Fascinating Problem / The Problem of Sentiment Analysis / Document Sentiment Classification / Sentence Subjectivity and Sentiment Classification / Aspect-Based Sentiment Analysis / Sentiment Lexicon Generation / Opinion Summarization / Analysis of Comparative Opinions / Opinion Search and Retrieval / Opinion Spam Detection / Quality of Reviews / Concluding Remarks / Bibliography / Author Biography

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ABSTRACT

Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research 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 others 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, researchers 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 studentsZhiyuan 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 Zhangfor 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 ChengXiang 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.

Author Biography

Bing Liu is a professor of Computer Science at the University of Illinois at Chicago (UIC). He received his PhD in Artificial Intelligence from the University of Edinburgh. Before joining UIC, he was with the National University of Singapore. His current research interests are sentiment analysis and opinion mining, opinion spam detection, and social media modeling. He has published extensively in leading conferences and journals on these topics, e.g., ACL, EMNLP, COLING, KDD, WWW, AAAI, IJCAI, SIGIR, Computational Linguistics and ACM Transactions on Intelligent Systems and Technology. He has also given more than thirty invited and keynote speeches. Due to his work on opinion spam detection, he was featured in a front page article of The New York Times on Jan 27, 2012. Prof. Liu's earlier research was in the fields of data mining, Web mining and machine learning, where he also published numerous papers in prestigious conferences and journals, e.g., KDD, WWW, ICML, AAAI, IJCAI, ICDM, WSDM, and IEEE Transactions on Knowledge and Data Engineering. He has written a textbook titled Web Data Mining: Exploring Hyperlinks, Contents and Usage Data published by Springer (first edition in 2006, and second edition in 2011). Due to his research achievements, he has served as program chairs of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), IEEE International Conference on Data Mining (ICDM), ACM Conference on Web Search and Data Mining (WSDM), SIAM Conference on Data Mining (SDM), ACM Conference on Information and Knowledge Management (CIKM), and Pacific Asia Conference on Data Mining (PAKDD). He has also served extensively as areas chairs, track chairs, and senior program committee members for natural language processing, data mining, Web technology, and Artificial Intelligence conferences. Additionally, he has been on the editorial boards of many leading journals including Data Mining and Knowledge Discovery (DMKD), ACM Transactions on the Web (TWEB), and IEEE Transactions on Knowledge and Data Engineering (TKDE).

Bibliography

Abbasi, Ahmed, Hsinchun Chen, and Arab Salem. Sentiment analysis in multiple languages: Feature selection for opinion classification in web forums. ACM Transactions on Information Systems (TOIS), 2008. 26(3). doi:10.1145/1361684.1361685

Abdul-Mageed, Muhammad, Mona T. Diab, and Mohammed Korayem. Subjectivity and sentiment analysis of modern standard Arabic In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics:shortpapers. 2011.

Akkaya, Cem, Janyce Wiebe, and Rada Mihalcea. Subjectivity word sense disambiguation. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing (EMNLP-2009). 2009. doi:10.3115/1699510.1699535

Alm, Ebba Cecilia Ovesdotter. Affect in text and speech, 2008: ProQuest.

Andreevskaia, Alina and Sabine Bergler. Mining WordNet for fuzzy sentiment: Sentiment tag extraction from WordNet glosses. In Proceedings of Conference of the European Chapter of the Association for Computational Linguistics (EACL-06). 2006.

Andreevskaia, Alina and Sabine Bergler. When specialists and generalists work together: Overcoming domain dependence in sentiment tagging. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL-2008). 2008.

Andrzejewski, David and Xiaojin Zhu. Latent Dirichlet Allocation with topic-in-set knowledge. In Proceedings of NAACL HLT. 2009. doi:10.3115/1621829.1621835

Andrzejewski, David, Xiaojin Zhu, and Mark Craven. Incorporating domain knowledge into topic modeling via Dirichlet forest priors. In Proceedings of ICML. 2009. doi:10.1145/1553374.1553378

Archak, Nikolay, Anindya Ghose, and Panagiotis G. Ipeirotis. Show me the money!: deriving the pricing power of product features by mining consumer reviews. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-2007). 2007.

Asher, Nicholas, Farah Benamara, and Yvette Yannick Mathieu. Distilling opinion in discourse: A preliminary study. In Proceedings of the International Conference on Computational Linguistics (C0LING-2008): Companion volume: Posters and Demonstrations. 2008.

Asur, Sitaram and Bernardo A. Huberman. Predicting the future with social media. Arxiv preprint arXiv:1003.5699, 2010. doi:10.1109/WI-IAT.2010.63

Aue, Anthony and Michael Gamon. Customizing sentiment classifiers to new domains: a case study. In

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