# Learning Lexico-Functional Patterns for First-Person Affect

arXiv: https://arxiv.org/abs/1708.09789 · HTML: https://mlsanity.com/paper/1708.09789/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2017.

## Built on by (most cited first, 1 of 1)

- [Acquiring Common Chinese Emotional Events Using Large Language Model](https://mlsanity.com/paper/2511.04989/) (2025)

## Abstract (arXiv)

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.