Chengqing Zong - Text Data Mining

Jointly published with Tsinghua University Press

The print edition is not for sale in China (Mainland). Customers from China (Mainland) please order the print book from: Tsinghua University Press.

Jointly published with Tsinghua University Press

This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd.

The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

We are living in the Big Data era. Over 80% of real-world data are unstructured in the form of natural language text, such as books, news reports, research articles, social media messages, and webpages. Although data mining and machine learning have been popular in data analysis, most data mining methods handle only structured or semistructured data. In comparison with mining structured data, mining unstructured text data is more challenging but will also play a more essential role in turning massive data into structured knowledge. It is no wonder we have witnessed such a dramatic upsurge in the research on text mining and natural language processing and their applications in recent years.

Text mining is a confluence of natural language processing, data mining, machine learning, and statistics used to mine knowledge from unstructured text. There have already been multiple textbooks dedicated to data mining, machine learning, statistics, and natural language processing. However, we seriously lack textbooks on text mining that systematically introduce important topics and up-to-date methods for text mining. This book, Text Data Mining, bridges this gap nicely. It is the first textbook, and a brilliant one, on text data mining that not only introduces foundational issues but also offers comprehensive and state-of-the-art coverage of the important and ongoing research themes on text mining. The in-depth treatment of a wide spectrum of text mining themes and clear introduction to the state-of-the-art deep learning methods for text mining make the book unique, timely, and authoritative. It is a great textbook for graduate students as well as a valuable handbook for practitioners working on text mining, natural language processing, data mining, and machine learning and their applications. This book is written by three pioneering researchers and highly reputed experts in the fields of natural language processing and text mining. The first author has written an authoritative and popular textbook on natural language processing that has been adopted as a standard textbook for university undergraduate and first-year graduate students in China. However, this new text mining book has completely different coverage from his NLP textbook and offers new and complementary text mining themes. Both books can be studied independently, although I would strongly encourage students working on NLP and text mining to study both.

This text mining book starts with text preprocessing, including both English and Chinese text preprocessing, and proceeds to text representation, covering the vector space model and distributed representation of words, phrases, sentences, and documents, in both statistical modeling and deep learning models. It then introduces feature selection methods, statistical learning methods, and deep neural network methods, including multilayer feed-forward neural networks, convolutional neural networks, and recurrent neural networks, for document classification. It next proceeds to text clustering, covering sample and cluster similarities, various clustering methods, and clustering evaluation. After introducing the fundamental theories and methods of text mining, the book uses five chapters to cover a wide spectrum of text mining applications, including topic modeling (which is also treated as a fundamental issue from some viewpoints but can be used independently), sentiment analysis and opinion mining, topic detection and tracking, information extraction, and automated document summarization. These themes are active research frontiers in text mining and are covered comprehensively and thoroughly, with a good balance between classical methods and recent developments, including deep learning methods.

As a data mining researcher, I have recently been deeply involved in text mining due to the need to handle the large scale of real-world data. I could not find a good text mining textbook written in English or Chinese to learn and teach. It is exciting to see that this book provides such a comprehensive and cutting-edge introduction. I believe this book will benefit data science researchers, graduate students, and those who want to include text mining in practical applications. I loved reading this book and recommend it highly to everyone who wants to learn text mining!