Natural language processing recipes unlocking text data with machine learning and deep learning using Python
| Otros Autores: | , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
[Place of publication not identified] :
Apress
[2021]
|
| Edición: | Second edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009633549606719 |
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
- Step 4-2. Create a custom function for text standardization
- Step 4-3. Run the text_std function
- Recipe 2-5. Correcting Spelling
- Problem
- Solution
- How It Works
- Step 5-1. Read/create the text data
- Step 5-2. Execute spelling correction on the text data
- Recipe 2-6. Tokenizing Text
- Problem
- Solution
- How It Works
- Step 6-1. Read/create the text data
- Step 6-2. Tokenize the text data
- Recipe 2-7. Stemming
- Problem
- Solution
- How It Works
- Step 7-1. Read the text data
- Step 7-2. Stem the text
- Recipe 2-8. Lemmatizing
- Problem
- Solution
- How It Works
- Step 8-1. Read the text data
- Step 8-2. Lemmatize the data
- Recipe 2-9. Exploring Text Data
- Problem
- Solution
- How It Works
- Step 9-1. Read the text data
- Step 9-2. Import necessary libraries.
- Step 9-3 Check the number of words in the data
- Step 9-4. Compute the frequency of all words in the reviews
- Step 9-5. Consider words with length greater than 3 and plot
- Step 9-6. Build a word cloud
- Recipe 2-10. Dealing with Emojis and Emoticons
- Problem
- Solution
- How It Works
- Step 10-A1. Read the text data
- Step 10-A2. Install and import necessary libraries
- Step 10-A3. Write a function that coverts emojis into words
- Step 10-A4. Pass text with an emoji to the function
- Problem
- Solution
- How It Works
- Step 10-B1. Read the text data
- Step 10-B2. Install and import necessary libraries
- Step 10-B3. Write a function to remove emojis
- Step 10-B4. Pass text with an emoji to the function
- Problem
- Solution
- How It Works
- Step 10-C1. Read the text data
- Step 10-C2. Install and import necessary libraries
- Step 10-C3. Write function to convert emoticons into word
- Step 10-C4. Pass text with emoticons to the function
- Problem
- Solution
- How It Works
- Step 10-D1 Read the text data
- Step 10-D2. Install and import necessary libraries
- Step 10-D3. Write function to remove emoticons
- Step 10-D4. Pass text with emoticons to the function
- Problem
- Solution
- How It Works
- Step 10-E1. Read the text data
- Step 10-E2. Install and import necessary libraries
- Step 10-E3. Find all emojis and determine their meaning
- Recipe 2-11. Building a Text Preprocessing Pipeline
- Problem
- Solution
- How It Works
- Step 11-1. Read/create the text data
- Step 11-2. Process the text
- Chapter 3: Converting Text to Features
- Recipe 3-1. Converting Text to Features Using One-Hot Encoding
- Problem
- Solution
- How It Works
- Step 1-1. Store the text in a variable
- Step 1-2. Execute a function on the text data
- Recipe 3-2. Converting Text to Features Using a Count Vectorizer
- Problem.
- Solution
- How It Works
- Recipe 3-3. Generating n-grams
- Problem
- Solution
- How It Works
- Step 3-1. Generate n-grams using TextBlob
- Step 3-2. Generate bigram-based features for a document
- Recipe 3-4. Generating a Co-occurrence Matrix
- Problem
- Solution
- How It Works
- Step 4-1. Import the necessary libraries
- Step 4-2. Create function for a co-occurrence matrix
- Step 4-3. Generate a co-occurrence matrix
- Recipe 3-5. Hash Vectorizing
- Problem
- Solution
- How It Works
- Step 5-1. Import the necessary libraries and create a document
- Step 5-2. Generate a hash vectorizer matrix
- Recipe 3-6. Converting Text to Features Using TF-IDF
- Problem
- Solution
- How It Works
- Step 6-1. Read the text data
- Step 6-2. Create the features
- Recipe 3-7. Implementing Word Embeddings
- Problem
- Solution
- How It Works
- skip-gram
- Continuous Bag of Words (CBOW)
- Recipe 3-8. Implementing fastText
- Problem
- Solution
- How It Works
- Recipe 3-9. Converting Text to Features Using State-of-the-Art Embeddings
- Problem
- Solution
- ELMo
- Sentence Encoders
- doc2vec
- Sentence-BERT
- Universal Encoder
- InferSent
- Open-AI GPT
- How It Works
- Step 9-1. Import a notebook and data to Google Colab
- Step 9-2. Install and import libraries
- Step 9-3. Read text data
- Step 9-4. Process text data
- Step 9-5. Generate a feature vector
- Sentence-BERT
- Universal Encoder
- Infersent
- Open-AI GPT
- Step 9-6. Generate a feature vector function automatically using a selected embedding method
- Chapter 4: Advanced Natural Language Processing
- Recipe 4-1. Extracting Noun Phrases
- Problem
- Solution
- How It Works
- Recipe 4-2. Finding Similarity Between Texts
- Solution
- How It Works
- Step 2-1. Create/read the text data
- Step 2-2. Find similarities
- Phonetic Matching.
- Recipe 4-3. Tagging Part of Speech
- Problem
- Solution
- How It Works
- Step 3-1. Store the text in a variable
- Step 3-2. Import NLTK for POS
- Recipe 4-4. Extracting Entities from Text
- Problem
- Solution
- How It Works
- Step 4-1. Read/create the text data
- Step 4-2. Extract the entities
- Using NLTK
- Using spaCy
- Recipe 4-5. Extracting Topics from Text
- Problem
- Solution
- How It Works
- Step 5-1. Create the text data
- Step 5-2. Clean and preprocess the data
- Step 5-3. Prepare the document term matrix
- Step 5-4. Create the LDA model
- Recipe 4-6. Classifying Text
- Problem
- Solution
- How It Works
- Step 6-1. Collect and understand the data
- Step 6-2. Text processing and feature engineering
- Step 6-3. Model training
- Recipe 4-7. Carrying Out Sentiment Analysis
- Problem
- Solution
- How It Works
- Step 7-1. Create the sample data
- Step 7-2. Clean and preprocess the data
- Step 7-3. Get the sentiment scores
- Recipe 4-8. Disambiguating Text
- Problem
- Solution
- How It Works
- Step 8-1. Import libraries
- Step 8-2. Disambiguate word sense
- Recipe 4-9. Converting Speech to Text
- Problem
- Solution
- How It Works
- Step 9-1. Define the business problem
- Step 9-2. Install and import necessary libraries
- Step 9-3. Run the code
- Recipe 4-10. Converting Text to Speech
- Problem
- Solution
- How It Works
- Step 10-1. Install and import necessary libraries
- Step 10-2. Run the code with the gTTs function
- Recipe 4-11. Translating Speech
- Problem
- Solution
- How It Works
- Step 11-1. Install and import necessary libraries
- Step 11-2. Input text
- Step 11-3. Run the goslate function
- Chapter 5: Implementing Industry Applications
- Recipe 5-1. Implementing Multiclass Classification
- Problem
- Solution
- How It Works
- Step 1-1. Get the data from Kaggle.
- Step 1-2. Import the libraries.
