# How to improve the binary classification model for text (News Articles) of Recurrent Neural Net with word emmbeding?

I am trying to do binary classification of news articles using Recurrent Neural Net with word embedding. Following are the parameters of the model:

Data:
8000 labelled news articles (Sports:Non-sports::15:85)

Parameters:
embedding size = 128
vocabulary size = 100000
No. of LSTM cell in each layer = 128
No. of hidden layers = 2
batch size = 16
epochs = 10000

Result:
AUC on training set = 0.60
AUC on testing set = 0.55


As the both training and testing error is high model is underfitting and require more data. So I have couple of doubts here:

1. What would be the optimum data size required?
2. Can we change the parameters to improve AUC. By decreasing, embedding size or No. of neurons we can minimize degree of freedom.

The more the better. From my experience in text classification, 8000 samples should yield good results in most cases. AUC on testing set = 0.55 means your classifier is almost random: I would expect better.