I'm working with sentiment analysis using NB classifier. I've found some information (blogs, tutorials etc) that training corpus should be balanced:
My question is:
Why corspus should be balanced? The Bayes theorem is based on propability of reason/case. So for training purpose isn't it important that in real world for example negative tweets are only 10% not 33.3%?
You are correct, balancing data is important for many discriminative models, but not really for NB.
However, it might be still more beneficial to bias P(y) estimators to get better predictive performance (since due to various simplifications models use, probability assigned to minority class can be heaviy underfitted). For NB it is not about balancing data, but literally modifying the estimated P(y) so that on the validation set accuracy is maximised.
In my opinion the best dataset for training purposes if a sample of the real world data that your classifier will be used with.
This is true for all classifiers (but some of them are indeed not suitable to unbalanced training sets in which cases you don't really have a choice to skew the distribution), but particularly for probabilistic classifiers such as Naive Bayes. So the best sample should reflect the natural class distribution.
Note that this is important not only for the class priors estimates. Naive Bayes will calculate for each feature the likelihood of predicting the class given the feature. If your bayesian classifier is built specifically to classify texts, it will use global document frequency measures (the number of times a given word occurs in the dataset, across all categories). If the number of documents per category in the training set doesn't reflect their natural distribution, the global term frequency of terms usually seen in unfrequent categories will be overestimated, and that of frequent categories underestimated. Thus not only the prior class probability will be incorrect, but also all the P(category=c|term=t) estimates.
