Development of Sentiment Crawler using Large Language Model (LLM)

Duration

3 days

Course Overview

This intensive 3-day course is designed to equip bank employees with the knowledge and skills necessary to develop a sentiment crawler using Large Language Models (LLMs). The course focuses on leveraging advanced NLP techniques to analyze and categorize sentiment in financial texts, with the ultimate goal of enhancing the bank's existing scorecards and risk models. Through a combination of theoretical foundations and hands-on exercises, participants will learn to implement, fine-tune, and deploy LLM-based sentiment analysis tools tailored to the banking industry's needs.

Course Objectives

By the end of this course, participants will be able to:

• Understand the fundamentals of Natural Language Processing (NLP) and its applications in banking
• Comprehend the architecture and capabilities of various Large Language Models, with a focus on GPT
• Implement data preprocessing techniques for text analysis in a financial context
• Fine-tune a GPT model for sentiment analysis using banking-specific data
• Develop a functional sentiment crawler capable of processing and analyzing financial texts
• Implement topic modeling to extract key themes from financial documents
• Design a scoring system to quantify sentiment for use in banking models
• Integrate sentiment analysis results into existing credit risk assessment models
• Evaluate and optimize the performance of NLP models

Day 1: Introduction and Foundations

Morning Session

• 1. Introduction to Natural Language Processing (NLP) and its applications in banking
• - Brief overview of NLP techniques
• - Importance of sentiment analysis in banking
• 2. Understanding Large Language Models
• - Overview of different LLMs (BERT, GPT, T5)
• - Deep dive into GPT architecture
• - Pros and cons of different LLMs

Afternoon Session

• 3. Hands-on: Setting up the development environment
• - Installing necessary libraries (PyTorch, Transformers)
• - Introduction to Hugging Face Transformers library
• 4. Preprocessing text data for sentiment analysis
• - Tokenization, cleaning, and normalization techniques
• - Hands-on exercise: Preprocessing sample banking text data

Day 2: Model Development and Implementation

Morning Session

• 5. Fine-tuning GPT for sentiment analysis
• - Understanding transfer learning and fine-tuning concepts
• - Hands-on: Fine-tuning GPT on the 10k human-read articles dataset

Afternoon Session

• 6. Implementing the sentiment crawler
• - Designing the crawler architecture
• - Hands-on: Building the sentiment crawler using the fine-tuned model
• 7. Sentiment classification and scoring
• - Implementing positive, negative, and neutral classification
• - Developing a scoring system for sentiment intensity

Day 3: Advanced Topics and Integration

Morning Session

• 8. Topic modeling with LLMs
• - Introduction to topic modeling techniques
• - Hands-on: Implementing topic modeling on banking articles
• 9. Integrating sentiment scores into banking scorecards/models
• - Strategies for incorporating sentiment data into existing models
• - Case study: Enhancing credit risk assessment with sentiment analysis

Afternoon Session

• 10. Model evaluation and optimization
• - Metrics for sentiment analysis and topic modeling
• - Techniques for improving model performance
• 11. Deployment considerations and best practices
• - Model serving options
• - Addressing regulatory and compliance concerns in AI banking applications
• 12. Wrap-up and Q&A session
• - Recap of key learnings
• - Discussion on potential applications in the bank

Programme scope, delivery arrangements and suitability are confirmed during enquiry.