IMDB Sentiment Analysis with Simple RNN – Deep Learning for Natural Language Processing

Project: IMDB Sentiment Analysis using Simple RNN
Author: Shailesh Gupta
GitHub: View Source Code

📌 Overview

This project applies a Recurrent Neural Network (RNN) to perform binary sentiment classification on movie reviews from the IMDB dataset. The model is trained to identify whether a review is positive or negative using sequential word embedding and deep learning techniques.

🎯 Problem Statement

Can a machine understand the sentiment behind human-written movie reviews?

We aim to build a model that can learn from text data and correctly classify the emotional tone of reviews using sequence modeling with RNNs.

🔍 Key Features

  • Binary classification: Positive vs. Negative review
  • Sequential modeling using Simple RNN layers
  • Preprocessing with tokenization and padding
  • Train/validation accuracy and loss tracking
  • Deployed using Streamlit for easy interaction

🛠️ Tools & Tech Stack

  • Python, TensorFlow, Keras
  • SimpleRNN layers for sequence modeling
  • IMDB Dataset from Keras
  • Streamlit (for model deployment)

📊 App Screenshot



📁 GitHub Repository

Explore the codebase, model architecture, training process, and UI logic:
github.com/sg2499/IMDB-Sentiment-Analysis-using-Simple-RNN

🌐 Live Demo

👉 Try the Sentiment Classifier App

💡 What I Learned

  • How RNNs handle sequential text data and learn from word embeddings
  • The importance of preprocessing in NLP: padding, truncation, vocab size
  • Deploying an NLP model in a user-friendly way using Streamlit

🙌 Stay Connected

If you liked this project, feel free to star the GitHub repo ⭐ or connect with me on LinkedIn.

More deep learning and NLP projects coming soon — stay tuned!

📌 Tags:

IMDB, Sentiment Analysis, RNN, Deep Learning, NLP, TensorFlow, Keras, Streamlit, Python, Sequence Modeling

Location: India

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