rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Text Analytics with Python
    sub title: A Practical Real-World Approach to Gaining Actionable Insights from your Data
  name:
    name part: Sarkar, Dipanjan.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxu
    date issued: 2016
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XXI, 385 p. 54 illus., 33 illus. in color. online resource.
  abstract: Derive useful insights from your data using Python. Learn the techniques related to natural language processing and text analytics, and gain the skills to know which technique is best suited to solve a particular problem. Text Analytics with Python teaches you both basic and advanced concepts, including text and language syntax, structure, semantics. You will focus on algorithms and techniques, such as text classification, clustering, topic modeling, and text summarization. A structured and comprehensive approach is followed in this book so that readers with little or no experience do not find themselves overwhelmed. You will start with the basics of natural language and Python and move on to advanced analytical and machine learning concepts. You will look at each technique and algorithm with both a bird's eye view to understand how it can be used as well as with a microscopic view to understand the mathematical concepts and to implement them to solve your own problems. This book: Provides complete coverage of the major concepts and techniques of natural language processing (NLP) and text analytics Includes practical real-world examples of techniques for implementation, such as building a text classification system to categorize news articles, analyzing app or game reviews using topic modeling and text summarization, and clustering popular movie synopses and analyzing the sentiment of movie reviews Shows implementations based on Python and several popular open source libraries in NLP and text analytics, such as the natural language toolkit (nltk), gensim, scikit-learn, spaCy and Pattern.
  table of contents: Chapter 1:Natural Language Basics -- Chapter 2:Python Refresher for Text Analytics -- Chapter 3:Text Processing -- Chapter 4:Text Classification -- Chapter 5:Text summarization and topic modeling -- Chapter 6:Text Clustering and Similarity analysis -- Chapter 7:Sentiment Analysis.-.
  note: by Dipanjan Sarkar.
  subject:
    topic: Computer science
  subject:
    topic: Programming languages (Electronic computers)
  subject:
    topic: Database management
  subject:
    topic: Data mining
  subject:
    topic: Computer Science
  subject:
    topic: Big Data
  subject:
    topic: Database Management
  subject:
    topic: Data Mining and Knowledge Discovery
  subject:
    topic: Programming Languages, Compilers, Interpreters
  related item:
    title info:
      title: Springer eBooks
  identifier: 9781484223888
  identifier: http://dx.doi.org/10.1007/978-1-4842-2388-8
  record info:
    record creation date: 161130
    record change date: 20161130112528.0
    record identifier: 978-1-4842-2388-8