From NLTK to BERT: Build Your First AI Sentiment Model (IMDB Dataset + Python Tutorial)
In this video, you will learn how to build a complete Sentiment Analysis system using BERT (Bidirectional Encoder Representations from Transformers) from scratch.
We start with the basics of NLP (Natural Language Processing), understand the limitations of traditional libraries like NLTK, and then move towards modern deep learning models like BERT.
What you will learn:
What is NLP and why it is important
Difference between NLTK and BERT
How Tokenizer works (Text → Numbers)
How Dataset is used for training
Training a BERT model using IMDB dataset
Understanding model predictions & confidence scores
Saving and loading models locally (No repeated downloads)
Real-time user input sentiment prediction
Advanced Concepts Covered:
Tokenization (input_ids, attention_mask, token_type_ids)
Truncation issues and how to fix them
Model confidence interpretation
Domain Adaptation (Finance, Medical, Legal, Social datasets)
Datasets you can try:
IMDB (Movie Reviews)
Financial PhraseBank (Finance)
TweetEval (Social Media)
LexGLUE (Legal)
PubMed (Medical)
🛠 Tech Stack:
Python
PyTorch
HuggingFace Transformers
Datasets Library
Code includes:
✔ Training pipeline
✔ Evaluation metrics (Accuracy + F1 Score)
✔ Local caching system
✔ Interactive user input system
This tutorial is perfect for:
Beginners in NLP
Students learning AI/ML
Developers building real-world AI applications
#NLP #BERT #MachineLearning #DeepLearning #Python #AI #HuggingFace #SentimentAnalysis