Natural language processing recipes : unlocking text data with machine learning and deep learning using Python /

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Focus on implementing end-to-end projects using Python and leverage state-of-the-art algorithms. This book teaches you to efficiently use a wide range of natural language processing (NLP) packages to: implement text classification, identify parts of speech, utilize topic modeling, text summarization...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Kulkarni, Akshay
Otros Autores: Shivananda, Adarsha
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berkeley, CA : Apress, 2021.
Edición:2nd ed.
Temas:
Natural language processing (Computer science)
Python (Computer program language)
Machine learning.
Natural Language Processing
Traitement automatique des langues naturelles.
Python (Langage de programmation)
Apprentissage automatique.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Intro
  • Table of Contents
  • About the Authors
  • About the Technical Reviewer
  • Acknowledgments
  • Introduction
  • Chapter 1: Extracting the Data
  • Introduction
  • Client Data
  • Free Sources
  • Web Scraping
  • Recipe 1-1. Collecting Data
  • Problem
  • Solution
  • How It Works
  • Step 1-1. Log in to the Twitter developer portal
  • Step 1-2. Execute query in Python
  • Recipe 1-2. Collecting Data from PDFs
  • Problem
  • Solution
  • How It Works
  • Step 2-1. Install and import all the necessary libraries
  • Step 2-2. Extract text from a PDF file
  • Recipe 1-3. Collecting Data from Word Files
  • Problem
  • Solution
  • How It Works
  • Step 3-1. Install and import all the necessary libraries
  • Step 3-2. Extract text from a Word file
  • Recipe 1-4. Collecting Data from JSON
  • Problem
  • Solution
  • How It Works
  • Step 4-1. Install and import all the necessary libraries
  • Step 4-2. Extract text from a JSON file
  • Recipe 1-5. Collecting Data from HTML
  • Problem
  • Solution
  • How It Works
  • Step 5-1. Install and import all the necessary libraries
  • Step 5-2. Fetch the HTML file
  • Step 5-3. Parse the HTML file
  • Step 5-4. Extract a tag value
  • Step 5-5. Extract all instances of a particular tag
  • Step 5-6. Extract all text from a particular tag
  • Recipe 1-6. Parsing Text Using Regular Expressions
  • Problem
  • Solution
  • How It Works
  • Tokenizing
  • Extracting Email IDs
  • Replacing Email IDs
  • Extracting Data from an eBook and Performing regex
  • Recipe 1-7. Handling Strings
  • Problem
  • Solution
  • How It Works
  • Replacing Content
  • Concatenating Two Strings
  • Searching for a Substring in a String
  • Recipe 1-8. Scraping Text from the Web
  • Problem
  • Solution
  • How It Works
  • Step 8-1. Install all the necessary libraries
  • Step 8-2. Import the libraries
  • Step 8-3. Identify the URL to extract the data
  • Step 8-4. Request the URL and download the content using Beautiful Soup
  • Step 8-5. Understand the website's structure to extract the required information
  • Step 8-6. Use Beautiful Soup to extract and parse the data from HTML tags
  • Step 8-7. Convert lists to a data frame and perform an analysis that meets business requirements
  • Step 8-8. Download the data frame
  • Chapter 2: Exploring and Processing Text Data
  • Recipe 2-1. Converting Text Data to Lowercase
  • Problem
  • Solution
  • How It Works
  • Step 1-1. Read/create the text data
  • Step 1-2. Execute the lower() function on the text data
  • Recipe 2-2. Removing Punctuation
  • Problem
  • Solution
  • How It Works
  • Step 2-1. Read/create the text data
  • Step 2-2. Execute the replace() function on the text data
  • Recipe 2-3. Removing Stop Words
  • Problem
  • Solution
  • How It Works
  • Step 3-1. Read/create the text data
  • Step 3-2. Remove punctuation from the text data
  • Recipe 2-4. Standardizing Text
  • Problem
  • Solution
  • How It Works
  • Step 4-1. Create a custom lookup dictionary

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