We propose models for semantic orientations of phrases as well as classification methods based on the models. Although each phrase consists of multiple words, the semantic orientation of the phrase is not a mere sum of the orientations of the component words. Some words can invert the orientation. In order to capture the property of such phrases, we introduce latent variables into the models. Through experiments, we show that the proposed latent variable models work well in the classification of semantic orientations of phrases and achieved nearly 82% classification accuracy.
REF proposed to use based on latent variable models for sentiment classification of noun-adjective pairs.
Latent Variable Models For Semantic Orientations Of Phrases
Conference Of The European Association For Computational Linguistics
Computer Science