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
    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.

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.