Discovering and summarizing opinions from online reviews is an important and challenging task. A commonly-adopted framework generates structured review summaries with aspects and opinions. Recently topic models have been used to identify meaningful review aspects, but existing topic models do not identify aspect-specific opinion words. In this paper, we propose a MaxEnt-LDA hybrid model to jointly discover both aspects and aspect-specific opinion words. We show that with a relatively small amount of training data, our model can effectively identify aspect and opinion words simultaneously. We also demonstrate the domain adaptability of our model.
REF proposed the MaxEnt-LDA (a Maximum Entropy and LDA combination) hybrid model to jointly discover both aspect words and aspectspecific opinion words, which can leverage syntactic features to separate aspects and sentiment words.
Jointly Modeling Aspects and Opinions with a MaxEnt-LDA Hybrid
EMNLP
Computer Science