🎬 Movie Comment Sentiment Analyzer

Ever scrolled through movie comments and thought,
“Wait... is this person praising the movie or totally roasting it?”
Well, we built a model to find out.

💡 The Idea

We wanted to build a simple yet powerful sentiment analysis model
that can tell if a movie review is positive or negative — no guessing!

We used a dataset of 50,000 IMDb reviews and trained a deep learning model
to read between the lines and understand the real mood behind the words.

🧠 The Model

Here’s what’s going on under the hood:

• GloVe word embeddings to capture word meanings.
• A Bidirectional GRU model — because understanding context from both sides of a sentence is 🔑.
• A final Sigmoid layer for binary classification: Positive or Negative.

🎯 The Results (We're proud, okay?)

After all the data cleaning, tuning, testing, and a bit of suffering
(thanks, Colab GPU limits), we finally got:

Test Accuracy: 88.7%
Test Loss: 0.2748

And yes, that’s a solid accuracy score — especially for a model we built from scratch,
layer by layer, and refined manually till it hit just right.

🧰 Tech Stack

• Python 🐍
• TensorFlow & Keras
• GloVe (Global Vectors for Word Representation)
• NLTK
• NumPy & pandas
• Scikit-learn
• Matplotlib (for some cool visualizations)

⚙️ How to Run

1. Clone the repo:

   git clone https://github.com/Mennaateya/Movie-Comment-Sentiment-Analyzer.git
   cd Movie-Comment-Sentiment-Analyzer

2. Install the requirements:

   pip install -r requirements.txt

3. Open the notebook and run the model:

   jupyter notebook notebooks/sentiment_model_gru.ipynb

📊 Dataset

We used a publicly available dataset from Stanford (no worries, you won’t have to download anything yourself — we’ve got you covered).
You'll find the link inside the notebook!

👩‍💻 Built With Love By

Menna
Marym
Farah
Nada
Basmalla

📧 our Gmails:

mennaateya30@gmail.com
marym.ayman.mo@gmail.com
nadawahdan493@gmail.com

🔗 LinkedIn profiles:

• Menna : https://www.linkedin.com/in/menna-ateya/
• Marym : https://www.linkedin.com/in/marym-ayman-43aa0a26b
• Nada :
  https://www.linkedin.com/in/nada-wahdan-5227222b1
• basmala : https://www.linkedin.com/in/basmala-essam-67136b326
• farah :
  https://www.linkedin.com/in/farah-hazem-69976a22b

🪪 License

MIT License – feel free to use, modify, and share the project. Just give credit where credit is due 🤝