Practical Natural Language Processing with Python

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

NLP Search Reviews Tweets/FB Posts Chat data SMS data Content data 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