When performing logistic. regression on sentiment analysis, you represented each tweet is a vector of ones and

zeros. However your model did not work well. Your training cost was reasonable, but your testing cost was just not

acceptable. What could be a possible reason?

The vector representations are sparse and therefore it is much harder for your model to learn anything that

could generalize well to the test set.

You prubably need to increasc your vocabulary size because it seems like you have very little features.

Logistic regression does not work for sentiment analysis, and therefore you should be looking at other mod

els.

Sparse representations require a good amount of training time so you should train your modet for longer