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PoETIC: A Re-framing of Context Dependent Emotion Detection

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.