Karima277/Darija-Sentiment-Analysis

# Darija-Sentiment-Analysis A Python model for performing sentiment analysis on tweets written in Moroccan Darija. The module is built using TensorFlow and includes functionalities for preprocessing, training, evaluating, and testing a sentiment analysis model. ## Features - Preprocess tweetS by tokenizing and padding text data. - Build a bidirectional LSTM model with dropout regularization for binary classification (positive or negative sentiment). - Train the model with learning rate scheduling and evaluate its performance. - Visualize training performance and confusion matrices. - Test the model on custom sentences to predict sentiment and confidence scores. ## Dataset The dataset used is a collection of tweets in Moroccan Darija across various domains: - Initially formatted as `MSAC.arff` and converted to a `CSV` file for easier use. - Labels are encoded as `1` (Positive) and `0` (Negative). - Cleaned and balanced dataset with a total of **2000 tweets**: - **1000 positive** - **1000 negative** ## How to Use the model tkinzer and the saved model are provided you only need to install them and call them in a specific task ! or you can easily download the data + the ipynb file and try them out :) ### Prerequisites Make sure you have Python 3.7+ installed along with the following libraries: - `tensorflow` - `pandas` - `numpy` - `matplotlib` - `seaborn` - `scikit-learn` ## Application Screenshots Below are some screenshots of the application: ### Home Screen ### Sentiment Results