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Course Outline
Introduction to Sentiment Analysis
- Fundamentals of sentiment analysis
- Challenges and opportunities in sentiment analysis
- Overview of LLMs and their capabilities
LLMs and Natural Language Understanding
- Deep dive into LLMs architecture
- Understanding context and sentiment with LLMs
- Preprocessing data for sentiment analysis
Building Sentiment Analysis Models with LLMs
- Training LLMs for sentiment analysis
- Fine-tuning models for specific domains
- Practical exercises on model training
Analyzing Social Media with LLMs
- Collecting social media data for analysis
- Real-time sentiment tracking on social platforms
- Case studies of social sentiment analysis
Sentiment Analysis in Customer Feedback
- Extracting insights from customer reviews and surveys
- Enhancing customer service with sentiment analysis
- Workshop on feedback analysis
Advanced Topics in Sentiment Analysis
- Addressing sarcasm, irony, and complex emotions
- Cross-language sentiment analysis
- Future trends in sentiment analysis with LLMs
Ethical Considerations and Bias Mitigation
- Ethical implications of sentiment analysis
- Identifying and mitigating bias in models
- Responsible use of sentiment analysis
Project and Assessment
- Analyzing sentiment from a chosen dataset
- Peer reviews and group discussions
- Final assessment and feedback
Summary and Next Steps
Requirements
- An understanding of basic machine learning concepts
- Experience with text data preprocessing and analysis
- Familiarity with Python programming
Audience
- Data scientists and analysts
- Marketing professionals
- Product managers
21 Hours
