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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
- In-depth exploration of LLM 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
- A foundational understanding of basic machine learning concepts.
- Practical experience with text data preprocessing and analysis.
- Familiarity with Python programming.
Target Audience
- Data scientists and analysts.
- Marketing professionals.
- Product managers.
21 Hours
