We inspected the learned weights in the adapted models in each user from LinAdapt, and found the words like waste, poor, and good share the same sentiment polarity as in the global model but different magnitudes; while words like money, instead, and return are almost neutral in global model, but vary across the personalized models.
words such as care, sex, evil, pure, and correct constantly carry the same sen- timent across users.