LLMs for Sentiment Analysis Training Course
Large Language Models (LLMs) are deep neural network models that can generate natural language texts based on a given input or context.
This instructor-led, live training (online or onsite) is aimed at intermediate-level data and marketing professionals who wish to apply LLMs to analyze and interpret public sentiment from various text sources such as social media posts, product reviews, and customer feedback.
By the end of this training, participants will be able to:
- Understand the principles of sentiment analysis and its application using LLMs.
- Preprocess and prepare datasets for sentiment analysis.
- Train and fine-tune LLMs to accurately reflect sentiment in text.
- Analyze sentiment in real-time from social media and other text sources.
- Integrate sentiment analysis findings into business strategies and decision-making processes.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
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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
Open Training Courses require 5+ participants.
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