Year: 2026
Type: conference-paper
Abstract: Emotion classification has been extensively studied, with numerous datasets enabling progress in both textual and multimodal settings.However, most existing text-based resources treat emotion as an utterance-level property, assuming that the emotional content is fully encoded in the sentence itself.This assumption is problematic: in the absence of paralinguistic cues such as prosody, facial expressions, or emojis, textual emotions are often highly context-dependent.Many utterances lack explicit emotion markers, and even when present, such cues may be overridden by broader situational context.Sentence-level emotion annotation, thus, is driven by the annotator's ability to imagine the context in which the given utterance would elicit a given emotion.An utterance may be able to express an emotion completely (Emotion Obvious), or it can express an emotion when imagined in a certain context (Emotion Plausible).Also, for an utterance, certain emotions might be implausible to express given the specific wording of a sentence (Emotion-Implausible).To address these issues, we create a new paradigm for emotion classification by categorizing utterance and emotion pairs into context-dependency classes.We present the PoETIC benchmark dataset, where sentences in the GoEmotions dataset are human-annotated for the three aforementioned classes across seven emotions (Fear, Anger, Sadness, Joy, Disgust, Surprise, and Neutral).We observe that gold-tagged emotions in GoEmotions do not have a clear correlation with human judgment with respect to the ability to express other emotions, given different contexts.Human annotators identify significantly more plausible emotions for a given utterance if asked to imagine a plausible context per utterance-emotion pair.We also present baselines using three popular large language models and two "small" language models in zero-shot and few-shot settings on the benchmark dataset.
Authors: Nirmal Surange, Manish Shrivastava
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166556335