https://github.com/lifehome/ay2024-ufce3p-30-3
Simple Sentiment Analysis implementation for UFCE3P-30-3 coursework
https://github.com/lifehome/ay2024-ufce3p-30-3
Last synced: 5 months ago
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Simple Sentiment Analysis implementation for UFCE3P-30-3 coursework
- Host: GitHub
- URL: https://github.com/lifehome/ay2024-ufce3p-30-3
- Owner: lifehome
- License: agpl-3.0
- Created: 2025-05-06T13:18:43.000Z (5 months ago)
- Default Branch: master
- Last Pushed: 2025-05-07T06:03:53.000Z (5 months ago)
- Last Synced: 2025-05-07T06:34:30.646Z (5 months ago)
- Size: 15.6 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# AY2024-UFCE3P-30-3
Simple Sentiment Analysis implementation for UFCE3P-30-3 coursework
## Dataset reference
In this proejct we are using "depression-social-media" published on Kaggle.com under an unknown license.
- https://www.kaggle.com/datasets/nicohu/depressionsocialmedia/data
We would like to thank the uploading user and author(s), as well as the people contributing in this dataset.
## Methodology
We will first perform text-preprocessing with "Tokenization & cleaning" step, followed by stratified sampling against the training and testing set of data. After that, TF-IDF will be used to vectorize the text before further processed by each algorithm.
The selected algorithm for supervised classifiers in this project are:
1. Naïve Bayes - (Multinomial)
2. Logistic Regression
3. Support Vector Machine - (Linear)
4. k-Nearest Neighbors (KNN)
## Contributors / Authors
Please refer to:
https://github.com/lifehome/AY2024-UFCE3P-30-3/graphs/contributors