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

ISBN-13: 9781484262450 / Angielski / Miękka / 2020 / 253 str.

Mathangi Sri

Recenzje

"Each of the book's four chapters describes multiple approaches to the area of analysis, from simple or "classic" methods to more complex ML-based solutions. ... Sri's contribution fills that instructional gap with relevant and usable Python code examples." (Harry J. Foxwell, Computing Reviews, November 9, 2021)

Spis treści

Chapter 1: Text Data in Real Word

 

Chapter Goal: This chapter focuses on various types of text data. The information it offers and the commercial value that each of the data could potentially offer. Understanding of the data provides the reader the landscape that they are getting into

 

No of pages: 10

 

Sub -Topics

 

1. NLP
2. Search
3. Reviews
4. Tweets/FB Posts
5. Chat data
6. SMS data
7. Content data
8. IVR utterance data

 

Chapter 2: NLP in Customer Service

 

Chapter Goal: Case studies for problems in customer service and how they could be solved.

 

No of pages: 39

 

Sub - Topics

 

1. A quick overview of the customer service industry

 

2. Voice Calls

 

3. Chats.

 

4. Tickets Data

 

5. Email Data

 

6. Voice of customer analysis

 

7. Intent Mining

8. NPS/CSAT drivers

 

9.      Insights in Sales Chats

10.  Reasons for non purchase

11.  Survey Comment Analysis

12.  Mining Voice transcripts

 

 

Chapter 3: NLP in Online Reviews

 

      Chapter Goal: Case studies for problems in online reviews and how they could be solved.

 

No of pages: 39

 

Sub - Topics:

 

1. Sentiment Analysis

 

2. Emotion Mining

 

3. Approach 1 :Lexicon based approach

 

4. Approach 2 : Rules based approach

 

5. Approach 3 - Machine Learning based approach (Neural Network)

 

6. Attribute Extraction

 

Chapter 4: NLP in BFSI

 

Chapter Goal: case studies for problems in the banking industry

 

Sub - Topics:

 

1. NLP in Fraud

 

2. Method 1 (For extracting NER, popular libraries)

 

3. Method 2 (For extracting NER, rules based approach)

 

4. Method 3 (Classifier based approach using word embeddings and neural networks)

 

5. Other use cases of NLP in BFSI

 

6. Natural Language Generation in banks

 

No of pages: 47

 

 

 

Chapter 5: NLP in Virtual Assistants

 

Chapter Goal: Case study in building state of the art natural language bots

 

Sub- Topics

 

1. Overview

 

2. Approach 1 : The “Classic” approach using LSTMs

 

3. Approach 2 : Generating Responses

 

4. BERT

 

5. Further nuances in building conversational bots:

 

No of pages: 43

O autorze

Mathangi is a renowned data science leader in India. She has 11 patent grants and 20+ patents published in the area of intuitive customer experience, indoor positioning, and user profiles. She has 16+ years of proven track record in building world-class data science solutions and products. She is adept in machine learning, text mining, NLP technologies, and NLP tools. She has built data science teams across large organizations including Citibank, HSBC, and GE, and tech startups such as 247.ai, PhonePe, and Gojek. She advises start-ups, enterprises, and venture capitalists on data science strategy and roadmaps. She  is an active contributor on machine learning to many premier institutes in India. She is recognized as one of “The Phenomenal SHE” by the Indian National Bar Association in 2019.

Blurb

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.

You will:

• 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