Weakly Supervised Induction of Affective Events by Optimizing Semantic Consistency

Abstract

To understand narrative text, we must comprehend how people are affected by the events that they experience. For example, readers understand that graduating from college is a positive event (achievement) but being fired from one's job is a negative event (problem). NLP researchers have developed effective tools for recognizing explicit sentiments, but affective events are more difficult to recognize because the polarity is often implicit and can depend on both a predicate and its arguments. Our research investigates the prevalence of affective events in a personal story corpus, and introduces a weakly supervised method for large scale induction of affective events. We present an iterative learning framework that constructs a graph with nodes representing events and initializes their affective polarities with sentiment analysis tools as weak supervision. The events are then linked based on three types of semantic relations: (1) semantic similarity, (2) semantic opposition, and (3) shared components. The learning algorithm iteratively refines the polarity values by optimizing semantic consistency across all events in the graph. Our model learns over 100,000 affective events and identifies their polarities more accurately than other methods.