Foundations of Sentiment Analysis and LLMs
Module 1:
Introduction to Sentiment Analysis
- What is sentiment analysis?
- Use cases across industries
- Traditional vs LLM-based sentiment
approaches
Module 2:
Overview of Transformers and LLMs
- Introduction to BERT, RoBERTa,
DistilBERT, GPT
- Tokenization, embeddings, and
attention
- Hugging Face Transformers library
overview
Module 3:
Preparing Data for Sentiment Tasks
- Dataset formats (binary,
multi-class)
- Common datasets (IMDb, SST-2,
Amazon Reviews)
- Preprocessing: cleaning, labeling,
tokenizing
Module 4:
Inference with Pretrained LLMs
- Using BERT and RoBERTa for
sentiment inference
- Zero-shot sentiment analysis with
GPT and pipeline()
- Comparing outputs of different
models
Fine-tuning and Evaluation
Module 5:
Fine-Tuning Pretrained Models
- Fine-tuning BERT/RoBERTa on a
labeled dataset
- Understanding model architecture
for classification
- Setting up training loops with
Transformers and Trainer API
Module 6:
Evaluation and Error Analysis
- Metrics: accuracy, precision,
recall, F1-score
- Confusion matrix and classification
report
- Spotting overfitting and
underfitting
- Analyzing misclassified examples
Module 7:
Prompt-Based Sentiment Analysis
- What is prompt engineering?
- Few-shot prompting examples using
OpenAI or Hugging Face models
- Comparing performance: fine-tuned
vs prompted models
Module 8:
Advanced Use Cases
- Multi-language sentiment analysis
- Domain-specific fine-tuning (e.g.,
finance, healthcare)
- Handling sarcasm, irony, and
complex opinions
Deployment and Real-World Project
Module 9:
Model Deployment Options
- Batch vs real-time prediction
pipelines
- Exporting models (ONNX,
TorchScript)
- Serving models via API using
FastAPI or Gradio
Module 10:
Real-World Sentiment Analysis Exercises
Module 11:
Best Practices and Troubleshooting
- Managing bias and fairness in
sentiment models
- Dealing with unbalanced datasets
- Efficient inference with
quantization or distillation
Module 12:
Wrap-Up and Next Steps
- Review of all tools and techniques
covered
- Resources for continued learning
(datasets, tutorials, papers)
- Optional knowledge check or quiz
- Final Q&A and feedback
