Taglish-Emotion-Recognition-of-Students-during-COVID-19

A study that aims to unfold what emotions did Filipino students manifest during a year of Covid-19 quarantines.

Data and Preprocessing

• taken from tweeter with keywords relating to words such as 'aral'(study), 'school', 'online class', 'COVID-19' which consists of Taglish (tagalog-english) tweets.
• clean tweets -> translate
• annotate the emotions
• two sampling methods: oversampling with text augmentation, and undersampling

Models

• 100-d Glove word embedding
• Support Vector Machine, Bernoulli Naïve Bayes, and Bi-directional Gated Recurrent Unit with Attention mechanism
• compares SVM and BNB (commonly used algorithm for Tagalog Emotion Recognition) to a Neural network with attention mechanism
• experiment on the two sampling strategy with the three models