A project for CPTS 437 - Introduction to Machine Learning at WSU, where we implemented multiple traditional machine learning algorithms (along with a simple Neural Network) from scratch to classify tweets based on their sentiment.
Algorithms Tested:
- K Nearest Neighbor (using embedding model to convert tweet to vector space)
- Support Vector Machine
- Naive Bayes Predictor
- Neural Network
In the end, our Neural Network achieved the highest accuracy (~70%), which we are happy with due to implementing that algorithm ourselves. Next was SVM at 62% (but much slower 😢), KNN at 55%, and Naive Bayes at ~45% to ~50% (due to data processing issues)
