This project classifies tweets as happy or sad, based on the words used in the tweet. It expects training input as a n x p matrix where n is the number of tweets and p is the vocabulary size, and each entry is the number of times a word appeared in the tweet.
We used a voted average approach of a combination of methods - generative, discriminative, dimensionality reduction, and ensemble methods.
- PCA
- Naive Bayes
- SVM
- K-nearest neighbors
- Logistic Regression
- Weak Learner Ensembles
- Neural Net
Code for training the models is in prep_ensemble.m. It assumes that the training data is in the current directory saved as words_train.mat with an n x p matrix of training features and an n x 1 matrix of classifications (happy or sad).
For our report, we determined accuracy by holding out 10% of the training data and using it to test our voted model. This code is in proj_final_accuracy_testing.m.
We used DL_toolbox functions for training the neural net.
To test after models have been trained:
- run
predict_labels.mproviding the function inputs. The only input that matters is a matrix of word counts of the tweets to classify, provided in the same format as the training dataset. - predict_labels will compute the label for each test instance for all of the models, and take the voted majority
- the voted majority is returned as Y_hat
Training on 4500 tweets, we were able to achieve an accuracy of 0.8076 on the testing dataset and 0.80178 in the validation set.
Built by Andrew Murphy, Jason Kim, and Lucy Chai: Machine Learning final project.