Welcome to this repository! Here, we will walk through an example of applying sentiment analysis in a real project. In this project, we perform sentiment analysis on IMDb movie reviews using the TF-IDF technology. We achieved high accuracy on the training data, but also encountered the challenge of overfitting.

If you're interested in running this project on your local machine, follow these steps:

1. Clone this repository using the following command:

git clone https://github.com/Aliraqimustafa/SentimentAnalysis.git

2. Install the TensorFlow library:

pip install tensorflow

3. Download the IMDb dataset from here.

4. Place the downloaded dataset in the appropriate directory and update the file path in the main.ipynb file.

5. Open the main.ipynb notebook and run all the cells.

Alternatively, if you prefer to work on Kaggle, you can follow these steps:

1. Access the IMDb dataset on Kaggle from here.

2. Create a new notebook on Kaggle.

3. Copy all the cells from the main.ipynb notebook in this repository and paste them into your Kaggle notebook.

4. Run all the cells to execute the project.

Feel free to explore and experiment with the project to learn more about sentiment analysis and the TF-IDF technique. Happy analyzing!

Note: This project is intended for educational purposes and to provide practical insights into sentiment analysis. Make sure to adhere to data usage and licensing guidelines when working with external datasets like IMDb.