Brussels, Belgium Belgium
Dec. 10, 2012 to Dec. 10, 2012
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDMW.2012.23
Proper treatment of negation is an important characteristic of methods for sentiment analysis. However, while there is a growing body of research on the automatic resolution of negation, it is not yet clear as to how negation is best represented for different applications. To begin to address this issue, we review representation alternatives and present a state-of-the-art system for negation resolution that is interoperable across these schemes. By employing different configurations of this system as a component in a test bed for lexically-based sentiment classification, we demonstrate that the choice of representation can have a significant impact on downstream processing.
Syntactics, Motion pictures, Feature extraction, Labeling, Gold, Communities, Context
Emanuele Lapponi, Jonathon Read, Lilja Ovrelid, "Representing and Resolving Negation for Sentiment Analysis", ICDMW, 2012, 2013 IEEE 13th International Conference on Data Mining Workshops, 2013 IEEE 13th International Conference on Data Mining Workshops 2012, pp. 687-692, doi:10.1109/ICDMW.2012.23