Classical approaches to sentiment classification exploit only textual features in a given review and are not aware of the personality of the user or the public sentiment toward the target product. In this paper, we propose a model that can accurately estimate the sentiment polarity by referring to the user leniency and product popularity computed during testing. For decoding with this model, we adopt an approximate strategy called "two-stage decoding." Preliminary experimental results on two realworld datasets show that our method significantly improves classification accuracy over existing state-of-the-art methods.
REF estimate the sentiment polarity by referring to the user leniency and product popularity computed during testing.
Modeling User Leniency and Product Popularity for Sentiment Classification
IJCNLP
Computer Science