We investigate systems that identify opinion expressions and assigns polarities to the extracted expressions. In particular, we demonstrate the benefit of integrating opinion extraction and polarity classification into a joint model using features reflecting the global polarity structure. The model is trained using large-margin structured prediction methods.
The system is evaluated on the MPQA opinion corpus, where we compare it to the only previously published end-to-end system for opinion expression extraction and polarity classification. The results show an improvement of between 10 and 15 absolute points in F-measure.