EmotiSense is a Python-based project that analyzes emojis and predicts their associated emotions (positive, neutral, or negative) with confidence scores. You can use this tool via a simple console interface or a web-based interface powered by Streamlit.

• Emoji Emotion Prediction: Predicts the sentiment of an emoji (positive, neutral, or negative).
• Confidence Scores: Provides confidence levels for each prediction.
• Streamlit Web Interface: A user-friendly web interface to interact with the model.
• Pre-trained Model: Ready-to-use pre-trained model to skip the training step.

pip install -r requirements.txt

python main.py

streamlit run app.py

python model_train.py

Note: Retraining the model requires the emoji_df.csv dataset (or edit the csv_name in line9 of model_train.py), it may take some time depending on your machine's performance and size of new dataset.

• app.py: Contains the Streamlit-based version of the main code for a user-friendly web interface.
• emoji_classifier_model.pkl: Pre-trained model for emoji classification.
• emoji_df.csv: Contains the dataset with emojis, names, and their corresponding emotion labels.
• emoji_vectorizer.pkl: Pre-trained vectorizer for emoji text feature extraction.
• main.py: Contains the console-based version of the emoji emotion analysis code.
• model_train.py: Contains code for training the emoji emotion classification model using a dataset.
• requirements.txt: File containing all required Python modules.