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Natural language processing recipes : unlocking text data with machine learning and deep learning using Python /
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berkeley, CA :
Apress,
2021.
|
| Edición: | 2nd ed. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
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