Here I develop a sentiment classifier using a bidirectional stacked RNN with LSTM/GRU cells for the Twitter sentiment analysis dataset, which is available here.
For the development of the models, I experimented with the number of stacked RNNs, the number of hidden layers, type of cells, skip connections, gradient clipping and dropout probability. I use the Adam optimizer and the binary cross-entropy loss function and I transformed the predicted logits to probabilities using a sigmoid function.
For the best model I found:
- Compute precision, recall and F1 for each class.
- Plot the loss vs epochs curve and the ROC curve
My solution is implemented in PyTorch and the report is well documented. I also have a notebook with the preprocess of the data. For running them, I used the Google Colab with its GPU.
Note: I also utilize pre-trained word embeddings GloVe as the initial embeddings to input on your models.
You can check the Google Colab Notebooks here:
- Preprocessing of data:
- Bidirectional stacked RNN with LSTM/GRU:
