Bidirectional LSTM




Word Embeddings

We used Global Vector (GloVe) embeddings to embed our text data into numerical data. GloVe is an unsupervised learning algorithm developed at Stanford for obtaining vector representations for words.

Hyperparameters

We trained the model for 20 epochs using the Adam optimizer with a learning rate of 0.001.

Class Weights

We created and assigned class weights during training to account for the uneven distribution of training data, as there were far more "discuss" and "unrelated" articles than there were "agree" and "disagree" articles.