Coarse-Grained +/-Effect Word Sense Disambiguation for Implicit Sentiment Analysis

Recent work has addressed opinion inferences that arise when opinions are expressed toward +/-effect events, events that positively or negatively affect entities. Many words have mixtures of senses with different +/-effect labels, and therefore word sense disambiguation is needed to exploit +/-effect information for sentiment analysis. This paper presents a knowledge-based +/-effect coarse-grained sense disambiguation method based on selectional preferences modeled via topic models. The method achieves an overall accuracy of 0.83, which represents a significant improvement over three competitive baselines.