CROWDSOURCING A WORD–EMOTION ASSOCIATION LEXICON

TLDR

Emotion analysis has been limited by small lexicons, despite extensive work on word polarity. The study aims to use crowdsourcing to rapidly build a large, high‑quality word‑emotion and word‑polarity lexicon while addressing annotation challenges. The authors employ crowdsourcing with a word‑choice filter to prevent malicious entries, detect unfamiliar terms, capture sense‑level data, and test question wording to improve agreement. Experiments show that asking whether a term is associated with an emotion yields significantly higher inter‑annotator agreement than asking whether it evokes an emotion.

Abstract

Even though considerable attention has been given to the polarity of words (positive and negative) and the creation of large polarity lexicons, research in emotion analysis has had to rely on limited and small emotion lexicons. In this paper, we show how the combined strength and wisdom of the crowds can be used to generate a large, high‐quality, word–emotion and word–polarity association lexicon quickly and inexpensively. We enumerate the challenges in emotion annotation in a crowdsourcing scenario and propose solutions to address them. Most notably, in addition to questions about emotions associated with terms, we show how the inclusion of a word choice question can discourage malicious data entry, help to identify instances where the annotator may not be familiar with the target term (allowing us to reject such annotations), and help to obtain annotations at sense level (rather than at word level). We conducted experiments on how to formulate the emotion‐annotation questions, and show that asking if a term is associated with an emotion leads to markedly higher interannotator agreement than that obtained by asking if a term evokes an emotion.