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.
Pipelines and joint models are trained by using a large-margin prediction method to find the sentiment expressions in texts and to declare the polarity of the extracted expressions REF .
Extracting Opinion Expressions and Their Polarities – Exploration of Pipelines and Joint Models
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies
Computer Science