Preparing the model

The model preparation, depending on the method we will implement, might be more or less complex. In our case, we opt to start the first baseline model with a simple deep learning architecture (which was the standard approach at the time of the competition), including a word embeddings layer (using pretrained word embeddings) and one or more bidirectional LSTM layers. This architecture was a common choice at the time when this competition took place, and it is still a good option for a baseline for a text classification problem. LSTM stands for Long Short-Term Memory. It is a type of recurrent neural network architecture designed to capture and remember long-term dependencies in sequential data. It is particularly effective for text classification problems due to its ability to handle and model intricate relationships and dependencies in sequences of text.

For this, we will need to perform some comment data preprocessing (we also performed preprocessing when...