Lilly Tech Systems
Sentiment Analysis
Learn to automatically detect opinions, emotions, and attitudes in text. From simple rule-based methods with VADER and TextBlob to state-of-the-art BERT models, this course covers every major approach to sentiment analysis with hands-on Python code.
What You'll Learn
By the end of this course, you will be able to build sentiment analysis systems using multiple approaches, from simple to state-of-the-art.
Rule-Based Methods
Use VADER and TextBlob for quick, interpretable sentiment scoring without training data.
Machine Learning
Build classifiers with scikit-learn using TF-IDF features, Naive Bayes, and Logistic Regression.
Deep Learning
Fine-tune BERT and transformer models for state-of-the-art sentiment classification.
Aspect-Based SA
Go beyond document-level sentiment to identify opinions about specific aspects of products or services.
Course Lessons
Follow the lessons in order or jump to any topic you need.
1. Introduction
What is sentiment analysis? Understand the task, its applications, challenges, and the spectrum of approaches available.
2. Rule-Based Methods
Use VADER and TextBlob for sentiment scoring. Learn how lexicon-based approaches work and when to use them.
3. ML-Based Methods
Build sentiment classifiers with scikit-learn. Feature engineering with TF-IDF, Bag of Words, and n-grams.
4. Deep Learning
Fine-tune BERT for sentiment analysis using Hugging Face Transformers. Achieve state-of-the-art accuracy.
5. Aspect-Based SA
Extract sentiment about specific aspects (price, quality, service) from reviews and social media posts.
6. Best Practices
Handle social media text, sarcasm, multilingual sentiment, production deployment, and common pitfalls.
Prerequisites
What you need before starting this course.
- Basic Python programming knowledge
- Familiarity with pandas and NumPy (helpful)
- Python 3.8+ installed with pip
- Basic understanding of machine learning concepts (for lessons 3-5)