Sentiment Analysis Label Distribution I am working on Sentiment Analysis model.
The dataset I have has three labels POSITIVE , NEGATIVE and NEUTRAL

But the problem is the data is not equal for labels.
Say out of 100K , 75 K are neutral, 15K positive and 10K negative.

I wanted to know whether it is necessary to chose equal distribution of labels while training or I can go ahead with unequal data and if so till what extent.
Are there any ways to deal with such problem.
For training, close to equal distributed data will give you better results. 

Type of data that you have, generally produced a biased model towards "neutral" class.

Are there any ways to deal with such problem?

I generally perform oversampling of the minority classes, such that for training(only),  have sufficient uniform count of data set.
 SMOTE, ADASYN are the few techniques of oversampling.