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Duration 21 hours
Course Outline
Foundations of Sentiment Analysis
- Core concepts and fundamentals of sentiment analysis
- Navigating challenges and identifying opportunities in sentiment analysis
- Brief overview of LLMs and their key capabilities
LLMs and Natural Language Understanding
- In-depth exploration of LLM architectures
- Leveraging LLMs to understand context and sentiment
- Data preprocessing techniques specifically for sentiment analysis
Developing Sentiment Analysis Models with LLMs
- Training LLMs specifically for sentiment analysis tasks
- Fine-tuning models to address specific domain requirements
- Practical exercises focused on model training processes
Applying LLMs to Social Media Analysis
- Strategies for collecting social media data for analysis
- Implementing real-time sentiment tracking on social platforms
- Reviewing case studies on social sentiment analysis
Sentiment Analysis in Customer Feedback
- Deriving actionable insights from customer reviews and surveys
- Improving customer service interactions through sentiment analysis
- Participating in workshops on feedback analysis techniques
Advanced Concepts in Sentiment Analysis
- Handling sarcasm, irony, and complex emotional expressions
- Techniques for cross-language sentiment analysis
- Exploring future trends in sentiment analysis powered by LLMs
Ethical Considerations and Bias Mitigation
- Evaluating the ethical implications of sentiment analysis
- Identifying and mitigating potential biases in models
- Ensuring responsible and ethical use of sentiment analysis
Project and Assessment
- Analyzing sentiment using a selected dataset
- Engaging in peer reviews and group discussions
- Completing the final assessment and receiving feedback
Summary and Future Directions
Requirements
- A solid understanding of fundamental machine learning concepts
- Practical experience with text data preprocessing and analysis
- Proficiency in Python programming
Target Audience
- Data scientists and analysts
- Marketing professionals
- Product managers
