Sentiment Analysis

Sentiment Analysis

Build systems that understand opinion and emotion in text. Text preprocessing, feature extraction (TF-IDF, embeddings), classical ML classifiers, fine-tuning transformer models (BERT, RoBERTa), evaluation metrics, and deploying NLP pipelines to production.

FundamentalsTopics 1–10
  • ·What is Sentiment Analysis?
  • ·Common Use Cases
  • ·Text Preprocessing
  • ·Regex for Text Cleaning
  • ·Bag of Words (BoW)
  • ·TF-IDF Explained
  • ·Lexicon Methods (VADER)
  • ·Naive Bayes Classifier
  • ·Evaluation Metrics
  • ·Handling Imbalance
Start Fundamentals →
IntermediateTopics 1–10
  • ·TF-IDF in Depth
  • ·Logistic Regression
  • ·SVM for Text
  • ·Feature Engineering
  • ·N-Gram Models
  • ·Feature Selection
  • ·Scikit-Learn Pipelines
  • ·Cross-Validation
  • ·Error Analysis
  • ·Multi-Class Sentiment
Start Intermediate →
AdvancedTopics 1–10
  • ·Word Embeddings
  • ·BERT Architecture
  • ·Hugging Face Setup
  • ·Fine-Tuning BERT
  • ·HF Tokenizers
  • ·DistilBERT for Speed
  • ·Domain-Specific Tuning
  • ·Aspect-Based Sentiment
  • ·Zero-Shot Classification
  • ·Confusion Matrix
Start Advanced →
ProductionTopics 1–10
  • ·Model Serving (FastAPI)
  • ·Batching Requests
  • ·Quantization (int8)
  • ·ONNX Export
  • ·Dockerization
  • ·Monitoring Drift
  • ·A/B Testing
  • ·Dataset Curation
  • ·Handling Edge Cases
  • ·CI/CD for NLP
Start Production →