Mining Text Data

  • Charu C. Aggarwal
  • ChengXiang Zhai

Table of contents

  1. Front Matter
    Pages i-xi
  2. Charu C. Aggarwal, ChengXiang Zhai
    Pages 1-10
  3. Jing Jiang
    Pages 11-41
  4. Ani Nenkova, Kathleen McKeown
    Pages 43-76
  5. Charu C. Aggarwal, ChengXiang Zhai
    Pages 77-128
  6. Charu C. Aggarwal, ChengXiang Zhai
    Pages 163-222
  7. Weike Pan, Erheng Zhong, Qiang Yang
    Pages 223-257
  8. Yizhou Sun, Hongbo Deng, Jiawei Han
    Pages 259-295
  9. Charu C. Aggarwal
    Pages 297-321
  10. Jian-Yun Nie, Jianfeng Gao, Guihong Cao
    Pages 323-359
  11. Zheng-Jun Zha, Meng Wang, Jialie Shen, Tat-Seng Chua
    Pages 361-384
  12. Xia Hu, Huan Liu
    Pages 385-414
  13. Bing Liu, Lei Zhang
    Pages 415-463
  14. Matthew S. Simpson, Dina Demner-Fushman
    Pages 465-517
  15. Back Matter
    Pages 519-522

About this book


Text mining applications have experienced tremendous advances because of web 2.0 and social networking applications. Recent advances in hardware and software technology have lead to a number of unique scenarios where text mining algorithms are learned.

Mining Text Data introduces an important niche in the text analytics field, and is an edited volume contributed by leading international researchers and practitioners focused on social networks & data mining. This book contains a wide swath in topics across social networks & data mining. Each chapter contains a comprehensive survey including the key research content on the topic, and the future directions of research in the field. There is a special focus on Text Embedded with Heterogeneous and Multimedia Data which makes the mining process much more challenging. A number of methods have been designed such as transfer learning and cross-lingual mining for such cases.

Mining Text Data simplifies the content, so that advanced-level students, practitioners and researchers in computer science can benefit from this book. Academic and corporate libraries, as well as ACM, IEEE, and Management Science focused on information security, electronic commerce, databases, data mining, machine learning, and statistics are the primary buyers for this reference book.


Clustering Data mining Databases Embedded Heterogeneous Machine learning and e-commerce Mining text Multimedia data Networking applications Networks Social networks Text mining

Editors and affiliations

  • Charu C. Aggarwal
    • 1
  • ChengXiang Zhai
    • 2
  1. 1.Thomas J. Watson Research CenterIBMHawthorneUSA
  2. Urbana-ChampaignUniversity of IllinoisURBANAUSA

Bibliographic information

  • DOI
  • Copyright Information Springer Science+Business Media, LLC 2012
  • Publisher Name Springer, Boston, MA
  • eBook Packages Computer Science
  • Print ISBN 978-1-4614-3222-7
  • Online ISBN 978-1-4614-3223-4
  • Buy this book on publisher's site