Natural language processing recipes : unlocking text data with machine learning and deep learning using Python / Akshay Kulkarni, Adarsha Shivananda.

Format:

Book

Author/Creator:

Kulkarni, Akshay, author.

Shivananda, Adarsha, author.

Language:

English

Subjects (All):

Natural language processing (Computer science).

Physical Description:

1 online resource (302 pages)

Edition:

Second edition.

Place of Publication:

[Place of publication not identified] : Apress, [2021]

Summary:

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, sentiment analysis, information retrieval, and many more applications of NLP. The book begins with text data collection, web scraping, and the different types of data sources. It explains how to clean and pre-process text data, and offers ways to analyze data with advanced algorithms. You then explore semantic and syntactic analysis of the text. Complex NLP solutions that involve text normalization are covered along with advanced pre-processing methods, POS tagging, parsing, text summarization, sentiment analysis, word2vec, seq2seq, and much more. The book presents the fundamentals necessary for applications of machine learning and deep learning in NLP. This second edition goes over advanced techniques to convert text to features such as Glove, Elmo, Bert, etc. It also includes an understanding of how transformers work, taking sentence BERT and GPT as examples. The final chapters explain advanced industrial applications of NLP with solution implementation and leveraging the power of deep learning techniques for NLP problems. It also employs state-of-the-art advanced RNNs, such as long short-term memory, to solve complex text generation tasks. After reading this book, you will have a clear understanding of the challenges faced by different industries and you will have worked on multiple examples of implementing NLP in the real world. What You Will Learn * 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 of feature engineering * Understand and implement the concepts of information retrieval, text summarization, sentiment analysis, text classification, and other advanced NLP techniques leveraging machine learning and deep learning Who This Book Is For Data scientists who want to refresh and learn various concepts of natural language processing (NLP) through coding exercises

Contents:

Intro

Table of Contents

About the Authors

About the Technical Reviewer

Acknowledgments

Introduction

Chapter 1: Extracting the Data

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

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

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

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

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

Tokenizing

Extracting Email IDs

Replacing Email IDs

Extracting Data from an eBook and Performing regex

Recipe 1-7. Handling Strings

Replacing Content

Concatenating Two Strings

Searching for a Substring in a String

Recipe 1-8. Scraping Text from the Web

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

Step 1-1. Read/create the text data

Step 1-2. Execute the lower() function on the text data

Recipe 2-2. Removing Punctuation

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

Step 3-1. Read/create the text data

Step 3-2. Remove punctuation from the text data

Recipe 2-4. Standardizing Text

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

Step 5-1. Read/create the text data

Step 5-2. Execute spelling correction on the text data

Recipe 2-6. Tokenizing Text

Step 6-1. Read/create the text data

Step 6-2. Tokenize the text data

Recipe 2-7. Stemming

Step 7-1. Read the text data

Step 7-2. Stem the text

Recipe 2-8. Lemmatizing

Step 8-1. Read the text data

Step 8-2. Lemmatize the data

Recipe 2-9. Exploring Text Data

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

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

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

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

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

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

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

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

Recipe 3-3. Generating n-grams

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

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

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

Step 6-1. Read the text data

Step 6-2. Create the features

Recipe 3-7. Implementing Word Embeddings

skip-gram

Continuous Bag of Words (CBOW)

Recipe 3-8. Implementing fastText

Recipe 3-9. Converting Text to Features Using State-of-the-Art Embeddings

ELMo

Sentence Encoders

doc2vec

Sentence-BERT

Universal Encoder

InferSent

Open-AI GPT

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

Infersent

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

Recipe 4-2. Finding Similarity Between Texts

Step 2-1. Create/read the text data

Step 2-2. Find similarities

Phonetic Matching.

Recipe 4-3. Tagging Part of Speech

Step 3-1. Store the text in a variable

Step 3-2. Import NLTK for POS

Recipe 4-4. Extracting Entities from Text

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

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

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

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

Step 8-1. Import libraries

Step 8-2. Disambiguate word sense

Recipe 4-9. Converting Speech to Text

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

Step 10-1. Install and import necessary libraries

Step 10-2. Run the code with the gTTs function

Recipe 4-11. Translating Speech

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

Step 1-1. Get the data from Kaggle.

Step 1-2. Import the libraries.

Notes:

Description based on print version record.

Includes index.

ISBN:

9781523150915

1523150912

9781484273517

1484273516

OCLC:

1265462358