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Practical Natural Language Processing with Python: With Case Studies from Industries Using Text Data at Scale

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Work with natural language tools and techniques to solve real-world problems. This book focuses on how natural language processing (NLP) is used in various industries. Each chapter describes the problem and solution strategy, then provides an intuitive explanation of how different algorithms work and a deeper dive on code and output in Python. 

Practical Natural Language Processing with Python follows a case study-based approach. Each chapter is devoted to an industry or a use case, where you address the real business problems in that industry and the various ways to solve them. You start with various types of text data before focusing on the customer service industry, the type of data available in that domain, and the common NLP problems encountered. Here you cover the bag-of-words model supervised learning technique as you try to solve the case studies. Similar depth is given to other use cases such as online reviews, bots, finance, and so on. As you cover the problems in these industries you’ll also cover sentiment analysis, named entity recognition, word2vec, word similarities, topic modeling, deep learning, and sequence to sequence modelling. 

By the end of the book, you will be able to handle all types of NLP problems independently. You will also be able to think in different ways to solve language problems. Code and techniques for all the problems are provided in the book.

What You Will Learn

  • Build an understanding of NLP problems in industry
  • Gain the know-how to solve a typical NLP problem using language-based models and machine learning
  • Discover the best methods to solve a business problem using NLP - the tried and tested ones
  • Understand the business problems that are tough to solve 

Who This Book Is For

Analytics and data science professionals who want to kick start NLP, and NLP professionals who want to get new ideas to solve the problems at hand.

Author(s): Mathangi Sri
Publisher: Apress
Year: 2020

Language: English
Pages: 253
City: New York

Table of Contents
About the Author
About the Technical Reviewer
Acknowledgments
Introduction
Chapter 1: Types of Data
Search
Reviews
Social Media Posts/Blogs
Chat Data
Personal Chats
Business Chats and Voice Call Data
SMS Data
Content Data
IVR Utterance Data
Useful Information from Data
Chapter 2: NLP in Customer Service
Voice Calls
Chats
Tickets Data
Email data
Voice of Customer
Intent Mining
Top Words to Understand Intents
Word Cloud
Rules to Classify Topics
Supervised Learning Using Machine Learning
Getting Manually Labelled Data
Word Tokenization
Term-Document Matrix
Data Normalization
Replacing Certain Patterns
Identifying Issue Lines
Top Customer Queries
Top CSAT Drivers
Top NPS Drivers
Insights into Sales Chats
Top Products for Sales Chats
Reasons for Non-Purchase
Survey Comments Analysis
Mining Voice Transcripts
Acoustic Model
Language Model
Chapter 3: NLP in Online Reviews
Sentiment Analysis
Emotion Mining
Approach 1: Lexicon-Based Approach
Approach 2: Rules-Based Approach
Observation 1
Observation 2
Observation 3
Observation 4
Overall Score
Implementing the Observations
Preprocessing
Booster and Negation Words (Observation 2 and Observation 3)
Exclamation Marks (Observation 4)
Evaluation of full_txt and Summary (Observation 1)
Optimizing the Code
Sentiment Analysis Libraries
Approach 3: Machine-Learning Based Approach (Neural Network)
Corpus Features
Building the Neural Network
Things to Improve
Attribute Extraction
Step 1: Using Regex to Normalize
Step 2: Extracting Noun Forms
Step 3: Creating a Mapping File
Step 4: Mapping Each Review to an Attribute
Step 5: Analyzing Brands
Chapter 4: NLP in Banking, Financial Services, and Insurance (BFSI)
NLP in Fraud
Method 1: Using Existing Libraries
Method 2: Extracting Noun Phrases
Method 3: Training Your Own Model
Word Embeddings
Word2Vec
CBOW
Other word2vec Libraries
Applying Word Embeddings to Supervised Learning
Method 3 – Approach 1
Method 3 – Approach 2
Applying the Model
Other Use Cases of NLP in Banking, Financial Services, and Insurance
SMS Data
Natural Language Generation in Banks
Chapter 5: NLP in Virtual Assistants
Types of Bots
The Classic Approach
Quick Overview of LSTM
Forget Gate
Input Gate
Output Gate
Applying LSTM
Time-Distributed Layer
Approach 2 - The Generating Responses Approach
Encoder-Decoder Architecture
Dataset
Implementing the Architecture
Encoder-Decoder Training
Encoder Output
Decoder Input
Preprocessing
Bidirectional LSTM
Encoder
Decoder
BERT
Language Models and Fine-Tuning
Overview of BERT
Fine-Tuning BERT for a Classifier
Further Nuances in Building Conversational Bots
Single-Turn vs. Multi-Turn Conversations
Multi-Lingual Bots
Index