《Text data mining》 offers thorough and detailed introduction to the fundamental theories and methods of text data mining, ranging from pre-processing (for both Chinese and English texts), text representation, feature selection, to text classification and text clustering. Also it presents predominant applications of text data mining, for example, topic model, sentiment analysis and opinion mining, topic detection and tracking, information extraction, and text automatic summarization, etc.

Chengqing Zong is professor at the National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences. He serves as chairs for many prestigious conferences such as ACL-IJCNLP, IJCAI, IJCAI-ECAI, AAAI and COLING, etc., and served as associate editors for prestigious journals such as TALLIP, Machine Translation, etc. He is the President of Asian Federation on Natural Language Processing and a member of International Committee on Computational Linguistics.

Preface With the rapid development and popularization of Internet and mobile communi- cation technologies, text data mining has attracted much attention. In particular, with the wide use of new technologies such as cloud computing, big data, and deep learning, text mining has begun playing an increasingly important role in many application ?elds, such as opinion mining and medical and ?nancial data analysis, showing broad application prospects. ¡¡Although I was supervising graduate students studying text classi?cation and automatic summarization more than ten years ago, I did not have a clear understand- ing of the overall concept of text data mining and only regarded the r...