The question is how good and what are some things to keep in mind when sentiment analysis models are tested on different datasets than they are trained on.
Say the task is to perform sentiment analysis on product reviews (unlabeled datset) - to classify positive, negative or neural. Because the data is unlabelled, a model can be trained (perhaps using logistic regression or NN) on a similar labeled dataset (say movie reviews, or product reviews) and tested on the original unlabelled dataset.
Will something like this work? Because the words of product names that occur in the unlabelled dataset will not be words that the model was exposed to during training, during test time will these words possibly throw off the model?