Mining Text Data 2012th Edition by Charu C. Aggarwal (PDF)

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Ebook Info

  • Published: 2012
  • Number of pages: 536 pages
  • Format: PDF
  • File Size: 11.13 MB
  • Authors: Charu C. Aggarwal

Description

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.

User’s Reviews

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐This is a very expensive book, costing five times what it should.The printing is very low quality – like it was a 30 year old newspaper. The binding is really cheap and frail. The diagrams are of very low quality – again, think newspapers from the 1980s.I will update this review once I’ve read the book properly but it seems to be full of dense text, lists of equations .. and doesn’t appear user friendly on first impression. Hope I’m wrong.

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