Natural language processing recipes unlocking text data with machine learning and deep learning using Python
You will: Know the core concepts of implementing NLP and various approaches to natural language processing (NLP), including NLP using Python libraries such as NLTK, textblob, SpaCy, Standford CoreNLP, and more Implement text pre-processing and feature engineering in NLP, including advanced methods o...
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| Other Authors: | |
| Format: | eBook |
| Language: | English |
| Published: |
Berkeley, CA
Apress
2021
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| Edition: | 2nd ed |
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| Online Access: | |
| Collection: | O'Reilly - Collection details see MPG.ReNa |
Table of Contents:
- 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
- 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
- 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
- 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