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
About the Author: 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.
