发帖
雷达卡
A Practical Real-World Approach to Gaining Actionable Insights from your Data
Authors: Dipanjan Sarkar
Complete coverage of the major concepts and techniques in natural language processing and text analytics
Several concrete and hands-on practical examples and implementation of various techniques which will enable readers to grasp the concepts better and keep them engaged
Implementations will be based on the popular programming language for data science, Python, and several popular open source libraries in NLP and text analytics
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
What you will learn:
• Natural Language concepts
• Analyzing Text syntax and structure
• Text Classification
• Text Clustering and Similarity analysis
• Text Summarization
• Semantic and Sentiment analysis
Table of contents
Front Matter
Pages i-xxi
Natural Language Basics
Pages 1-50
Python Refresher
Pages 51-106
Processing and Understanding Text
Pages 107-165
Text Classification
Pages 167-215
Text Summarization
Pages 217-263
Text Similarity and Clustering
Pages 265-317
Semantic and Sentiment Analysis
Pages 319-376
Back Matter
Pages 377-385
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