An Ontology of Emotion Process to Support Sentiment Analysis
Veda C. Storey, Eun Hee Park
Abstract
Sentiment analysis is used to mine text data from many sources, including blogs, support forums, and social media, in order to extract customers’ opinions and attitudes. The results can be used to make important assessments about a customer’s attitude toward a company and if and how a company should respond. However, much research on sentiment analysis uses simple classification, where the polarity of a text that is mined is classified as positive, negative, or neutral. This research creates an ontology of emotion process to support sentiment analysis, with an emphasis on obtaining a more fine-grained assessment of sentiment than polarity. The ontology is grounded in a theory of emotion process and consists of concepts that capture the generation of emotion all the way from the occurrence of an event to the resulting behaviors of the person expressing the sentiment. It includes two lexicons: one for affect and one for appraisal. The ontology is applied to posts obtained from customer support forums of large companies to show its applicability in a multilevel evaluation. Doing so provides an example of a complete ontology assessment effort.