This repository contains the Assignment Emotion project, which focuses on analysing and/or recognising human emotions using computational techniques. The project was developed as part of an academic assignment and demonstrates practical application of concepts such as data processing, emotion analysis, and model evaluation.
- To explore emotion-related data
- To apply appropriate algorithms or logic to classify or analyse emotions
- To evaluate the performance and outcomes of the approach used
- To demonstrate understanding of the techniques covered in the module
Assignment Emotion/ │ ├── data/ # Dataset(s) used for emotion analysis ├── src/ # Source code files ├── notebooks/ # Jupyter notebooks (if applicable) ├── results/ # Output files, graphs, or predictions ├── requirements.txt # Dependencies (if applicable) └── README.md # Project documentation
(Folder names may vary depending on implementation)
- Programming Language: Python (if applicable)
- Libraries/Tools:
- NumPy
- Pandas
- Matplotlib / Seaborn
- Scikit-learn
- Any other relevant libraries
git clone https://github.com/MuuFat/assignment-emotion.git cd assignment-emotion
pip install -r requirements.txt
Depending on the project structure: python main.py or open and run the Jupyter Notebook: jupyter notebook
- Data collection and preprocessing
- Feature extraction (if applicable)
- Emotion classification or analysis
- Evaluation of results
- Interpretation of findings
The results demonstrate the effectiveness of the chosen approach in identifying or analysing emotional patterns within the dataset. Outputs may include accuracy scores, confusion matrices, visualisations, or emotion predictions.
· Dataset size or quality constraints · Limited emotion categories · Model performance may vary depending on input data
·Expand dataset for better generalisation ·Experiment with alternative models or techniques ·Improve preprocessing and feature extraction ·Add a user interface or real-time emotion detection
MuuFat
This project is for educational purposes only.