From the Back Cover

Text analytics is a field that lies on the interface of information retrieval, machine learning, and natural language processing. This book carefully covers a coherently organized framework drawn from these intersecting topics. The chapters of this book span three broad categories: 1. Basic algorithms: Chapters 1 through 8 discuss the classical algorithms for text analytics such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis. 2. Domain-sensitive learning: Chapters 8 and 9 discuss learning models in heterogeneous settings such as a combination of text with multimedia or Web links. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. 3. Sequence-centric mining: Chapters 10 through 14 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, text summarization, information extraction, opinion mining, text segmentation, and event detection. This book covers text analytics and machine learning topics from the simple to the advanced. Since the coverage is extensive, multiple courses can be offered from the same book, depending on course level.

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About the Author

Charu C. Aggarwal is a Distinguished Research Staff Member (DRSM) at the IBMT. J. Watson Research Center in Yorktown Heights, New York. He completed his undergraduatedegree in Computer Science from the Indian Institute of Technology at Kanpurin 1993 and his Ph.D. from the Massachusetts Institute of Technology in 1996.He has worked extensively in the field of data mining. He has publishedmore than 350 papers in refereed conferences and journals andauthored over 80 patents. He is the author or editor of 17 books, includingtextbooks on data mining, recommender systems, and outlieranalysis. Because of the commercial value of his patents, he has thricebeen designated a Master Inventor at IBM. He is a recipient of an IBMCorporate Award (2003) for his work on bio-terrorist threat detectionin data streams, a recipient of the IBM Outstanding Innovation Award(2008) for his scientific contributions to privacy technology, and a recipientof two IBM Outstanding Technical Achievement Awards (2009, 2015) for his workon data streams/high-dimensional data. He received the EDBT 2014 Test of Time Awardfor his work on condensation-based privacy-preserving data mining. He is also a recipientof the IEEE ICDM Research Contributions Award (2015), which is one of the two highestawards for influential research contributions in the field of data mining.He has served as the general co-chair of the IEEE Big Data Conference (2014) and asthe program co-chair of the ACM CIKM Conference (2015), the IEEE ICDM Conference(2015), and the ACM KDD Conference (2016). He served as an associate editor of the IEEETransactions on Knowledge and Data Engineering from 2004 to 2008. He is an associateeditor of the IEEE Transactions on Big Data, an action editor of the Data Mining andKnowledge Discovery Journal, and an associate editor of the Knowledge and InformationSystems Journal. He has served as editor-in-chief of the ACM SIGKDD Explorations (2014–2017) and is currently an editor-in-chief of the ACM Transactions on Knowledge Discoveryfrom Data. He serves on the advisory board of the Lecture Notes on Social Networks, apublication by Springer. He has served as the vice-president of the SIAM Activity Groupon Data Mining and is a member of the SIAM industry committee. He is a fellow of theSIAM, ACM, and the IEEE, for “contributions to knowledge discovery and data miningalgorithms.”

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Text analytics is a field that lies on the interface of information retrieval,machine learning, and natural language processing, and this textbookcarefully covers a coherently organized framework drawn from these intersectingtopics. The chapters of this textbook is organized into three categories:

- Basic algorithms: Chapters 1 through 7 discuss the classical algorithmsfor machine learning from text such as preprocessing, similaritycomputation, topic modeling, matrix factorization, clustering,classification, regression, and ensemble analysis.

- Domain-sensitive mining: Chapters 8 and 9 discuss the learning methodsfrom text when combined with different domains such as multimedia andthe Web. The problem of information retrieval and Web search is alsodiscussed in the context of its relationship with ranking and machinelearning methods.

- Sequence-centric mining: Chapters 10 through 14 discuss varioussequence-centric and natural language applications, such as featureengineering, neural language models, deep learning, text summarization,information extraction, opinion mining, text segmentation, and eventdetection.

This textbook covers machine learning topics for text in detail. Since thecoverage is extensive,multiple courses can be offered from the same book,depending on course level. Even though the presentation is text-centric,Chapters 3 to 7 cover machine learning algorithms that are often used indomains beyond text data. Therefore, the book can be used to offercourses not just in text analytics but also from the broader perspective ofmachine learning (with text as a backdrop).

This textbook targets graduate students in computer science, as well as researchers, professors, and industrialpractitioners working in these related fields. This textbook is accompanied with a solution manual forclassroom teaching.