I have an extremely unbalanced dataset. https://cl.awaisathar.com/citation-sentiment-corpus/

Class POSITIVE:829

Class NEGATIVE:280

Class NEUTRAL: 7627

Here is my network:

```
import torch.nn as nn
class Sentiment_LSTM(nn.Module):
"""
We are training the embedded layers along with LSTM for the sentiment analysis
"""
def __init__(self, vocab_size, output_size, embedding_dim, hidden_dim, n_layers, drop_prob=0.5):
"""
Settin up the parameters.
"""
super(Sentiment_LSTM, self).__init__()
self.output_size = output_size
self.n_layers = n_layers
self.hidden_dim = hidden_dim
# embedding layer and LSTM layers
self.embedding = nn.Embedding(vocab_size, embedding_dim)
self.lstm = nn.LSTM(embedding_dim, hidden_dim, n_layers,
dropout=drop_prob, batch_first=True)
# dropout layer to avoida over fitting
self.dropout = nn.Dropout(0.5)
# linear and sigmoid layers
self.fc = nn.Linear(hidden_dim, output_size)
self.sig = nn.Sigmoid()
def forward(self, x):
"""
Perform a forward pass
"""
batch_size = x.size(0)
x = x.long()
embeds = self.embedding(x)
lstm_out, hidden = self.lstm(embeds)
# stack up lstm outputs
lstm_out = lstm_out.contiguous().view(-1, self.hidden_dim)
out = self.dropout(lstm_out)
out = self.fc(out)
# sigmoid function
sig_out = self.sig(out)
# reshape to be batch_size first
sig_out = sig_out.view(batch_size, -1,3)
#print("sig_out",sig_out.shape)
sig_out = sig_out[:, -1,:] # get last batch of labels
# return last sigmoid output and hidden state
return sig_out
```

Loss function:

```
lr=0.001
criterion = nn.BCELoss()
optimizer = torch.optim.Adam(net.parameters(), lr=lr)
```

My accuracy is low on the small classes. How can i improve it futher?