Sentiment Analysis with LLMs Training is a practical training program focused on using Large Language Models to analyze opinions, emotions, and attitudes in text data. This course explores how modern LLMs outperform traditional sentiment analysis techniques by understanding context, tone, and nuance. Participants will learn how LLM-powered sentiment analysis is applied across customer feedback, social media, surveys, and business intelligence systems.

• Understand sentiment analysis concepts using LLMs
• Analyze text data for emotions and opinions
• Apply LLMs for sentiment classification tasks
• Build AI workflows for text analytics
• Evaluate sentiment analysis accuracy and performance

• Data analysts and business analysts
• Marketing and customer experience teams
• Product and brand managers
• AI and NLP beginners
• Professionals working with text-based insights

• Basic understanding of text data and digital applications
• Familiarity with analytics or business intelligence concepts
• No prior machine learning or NLP experience required

• Understanding sentiment analysis concepts and approaches
• Using LLMs for sentiment detection and classification
• Analyzing emotions, tone, and intent in text
• Designing sentiment analysis workflows
• Interpreting and validating sentiment insights

By the end of this training, participants will be able to design and evaluate sentiment analysis solutions using Large Language Models. Learners will gain practical skills to extract actionable insights from text data and apply sentiment intelligence in business and analytical contexts.

Introduction to Sentiment Analysis
• What is sentiment analysis and opinion mining
• Traditional vs LLM-based sentiment analysis
• Business value of sentiment insights

Foundations of LLMs for Text Analysis
• How LLMs understand context and sentiment
• Tokenization and semantic understanding
• Zero-shot and few-shot sentiment analysis

Sentiment Categories and Models
• Binary, multi-class, and fine-grained sentiment
• Emotion detection and tone analysis
• Aspect-based sentiment analysis

Prompting Techniques for Sentiment Analysis
• Designing effective sentiment prompts
• Handling ambiguity and mixed sentiment
• Improving consistency and accuracy

Advanced Sentiment Analysis with LLMs
• Context-aware and domain-specific sentiment
• Handling sarcasm and nuanced language
• Multilingual sentiment analysis

Evaluation and Validation of Sentiment Results
• Accuracy, consistency, and bias analysis
• Human-in-the-loop validation
• Managing false positives and negatives

Real-World Applications of LLM Sentiment Analysis
• Customer feedback and reviews
• Social media and brand monitoring
• Employee surveys and internal communications

Ethics, Bias, and Responsible Sentiment Analysis
• Bias in sentiment interpretation
• Privacy and data sensitivity
• Ethical use of sentiment insights

Hands-on Sentiment Analysis Exercises
• Real-world sentiment datasets
• Prompt-driven sentiment workflows
• Scenario-based analysis and feedback

• Fundamentals of sentiment analysis
• LLM-based text classification
• Emotion and opinion detection techniques
• Sentiment analysis workflows
• Model evaluation and optimization

• Participation in hands-on sentiment exercises
• Scenario-based sentiment analysis assignments
• Knowledge and concept assessment

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Participants who successfully complete the training and evaluation will receive an AcadNXT Certificate of Completion in Sentiment Analysis with LLMs Training, validating their expertise in LLM-powered sentiment analysis.