Description
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
