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emotion annotations

Emotion annotations are descriptive metadata labels assigned to linguistic or multimodal data to specify the emotional states, categories, or affective associations they express or evoke. In computational linguistics and natural language processing, human annotators or automated systems assign these tags to units such as individual words, phrases, sentences, or larger texts according to defined emotional frameworks, which can include discrete categories like joy, sadness, fear, and anger, or continuous dimensions such as valence and arousal. These annotations serve as structured ground-truth datasets and lexicons essential for training, fine-tuning, and evaluating machine learning models used in sentiment analysis, emotion recognition, and affective computing.

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CROWDSOURCING A WORD–EMOTION ASSOCIATION LEXICON

CROWDSOURCING A WORD–EMOTION ASSOCIATION LEXICON

Saif M. Mohammad, Peter D. Turney

Why you should read this

Presents a practical crowdsourcing methodology for constructing large-scale word-emotion lexicons, proving that sense-verification questions and association-based framing substantially increase annotation quality and inter-annotator agreement.

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 identify instances where the annotator may not be familiar with the target term (allowing us to reject such annotations), and help 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 inter-annotator agreement than that obtained by asking if a term evokes an emotion.

Added

2026-09-14