Text Analytics with Python

Text Analytics with Python
  • eBook:
    Text Analytics with Python: A Practical Real-World Approach to Gaining Actionable Insights from your Data
  • Author:
    Dipanjan Sarkar
  • Edition:
    1 edition
  • Categories:
  • Data:
    December 1, 2016
  • ISBN:
  • ISBN-13:
  • Language:
  • Pages:
    412 pages
  • Format:
    PDF, ePUB

Book Description
Derive useful insights from your data using Python. You will learn both basic and advanced concepts, including text and language syntax, structure, and semantics. You will focus on algorithms and techniques, such as text classification, clustering, topic modeling, and text summarization.
Text Analytics with Python teaches you the techniques related to natural language processing and text analytics, and you will gain the skills to know which technique is best suited to solve a particular problem. 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.

What You Will Learn:
  • Understand the major concepts and techniques of natural language processing (NLP) and text analytics, including syntax and structure
  • Build a text classification system to categorize news articles, analyze app or game reviews using topic modeling and text summarization, and cluster popular movie synopses and analyze the sentiment of movie reviews
  • Implement Python and popular open source libraries in NLP and text analytics, such as the natural language toolkit (nltk), gensim, scikit-learn, spaCy and Pattern
Who This Book Is For:
IT professionals, analysts, developers, linguistic experts, data scientists, and anyone with a keen interest in linguistics, analytics, and generating insights from textual data


Chapter 1: Natural Language Basics 
Chapter 2: Python Refresher 
Chapter 3: Processing and Understanding Text 
Chapter 4: Text Classification 
Chapter 5: Text Summarization 
Chapter 6: Text Similarity and Clustering 
Chapter 7: Semantic and Sentiment Analysis 

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