Thank you again for the great answer, my model is improving bit by bit just from your input.
Last two questions, I promise:
1-  I’ve seen LSTM models for sentiment analysis that have a Conv1D layer or two before the BiLSTM layers. How would that fit here? I was under the impression convolutional layers are used in image, video, audio, etc. classification/recognition not in text.
2-  I’ve added l2 regularizers in both LSTM layers (kernel and bias). These help with overfitting and whatnot but would adding them cause any loss of patterns or info? Same for kernel and bias constraints, which is still unclear for me. Would adding the regularizers and constraints with the dropouts be overkill?