Amit Goyal, Department of Computer Science, University of Maryland, College Park, Maryland, USA; e-mail: amit@umiacs.umd.edu


This research revisits plot units, which were developed in the 1980s as a conceptual knowledge structure to represent the affect states of and emotional tensions between characters in narrative stories. We present a fully automated system, called AESOP, that generates plot unit representations for narrative texts. AESOP performs four steps: affect state recognition, character identification, affect state projection, and link creation. We also identify a type of knowledge that seems to be missing from existing lexical resources: verbs that impart positive or negative polarity onto their patients (e.g., “eat” imparts negative polarity because being eaten is bad, whereas “fed” imparts positive polarity because being fed is good). We develop two techniques to automatically harvest these “patient polarity verbs” (PPVs) from a Web corpus, and show that the PPVs improve affect state recognition. Finally, we evaluate AESOP’s performance on a set of fables, and present several analyses to shed light on the capabilities and limitations of current natural language processing technology for plot unit generation.