Learning Lexico-Functional Patterns for First-Person Affect
This work addresses the challenge of inferring implicit affect in informal narratives for applications in computational linguistics and sentiment analysis, representing an incremental advance over existing methods.
The paper tackled the problem of predicting affective reactions from first-person narratives, where explicit sentiment labels are often missing, by learning lexico-functional patterns from these texts. The result was an improvement in prediction accuracy from a baseline of 0.67 F-score to 0.75 F-score on a novel fine-grained test set.
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.