Learning Lexico-Functional Patterns for First-Person Affect

TL;DR

This paper introduces a method to learn lexico-functional patterns from first-person narratives to better predict affective reactions, improving over existing sentiment analysis baselines.

Contribution

It presents a novel approach to model affect in terms of lexical predicate functions learned from informal first-person narratives.

Findings

Improved affect prediction accuracy from 0.67F to 0.75F.

Constructed a fine-grained test set for affect inference.

Demonstrated the effectiveness of learned patterns over baseline methods.

Abstract

Informal first-person narratives are a unique resource for computational models of everyday events and people's affective reactions to them. People blogging about their day tend not to explicitly say I am happy. Instead they describe situations from which other humans can readily infer their affective reactions. However current sentiment dictionaries are missing much of the information needed to make similar inferences. We build on recent work that models affect in terms of lexical predicate functions and affect on the predicate's arguments. We present a method to learn proxies for these functions from first-person narratives. We construct a novel fine-grained test set, and show that the patterns we learn improve our ability to predict first-person affective reactions to everyday events, from a Stanford sentiment baseline of .67F to .75F.