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Beyond a Single Sentiment Score: Topical and Affective Dimensions of Emoji Meaning on Twitter

Rohan James, Ioannis Christofilogiannis, Yuuv Jauhari, Shreya Shah, Gauri Pethkar · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28337 · cited by 0 works (OpenAlex) · open-access PDF

Korean

우리는이 그림이 불완전하다고 주장합니다. 16{,}575 영어 트위터의 섬세한 코르푸스와 per-emoji 메트릭스에서 우리는 \emph{stability delta}라고 부릅니다. (단계 내에서 문장 삽입의 중간 클래스의 cosine 유사성) 의미는 단일 감정 점수로 요약됩니다. 우리는이 그림이 단단히 클러스터 된 주제적 맥락에서 사용 된 emojis가 또한 \emph{more} 폴라리스화 된 군중 소스 감정을 보여줍니다. 우리는 Kralj Novak et al.\ (2015) 감정 렉시콘에 대해 검증, 중앙 상관 관계는 Spearman $ρ = 0.361$ (Holm $p = 0.015$, 95% bootstrap CI $[+0.10, +

Machine translation (Meta M2M100); the English original follows.

Abstract

Emoji are widely treated as compact affective tokens whose meaning is summarised by a single sentiment score. We argue that this picture is incomplete. From a corpus of 16{,}575 English tweets and a per-emoji metric we call \emph{stability delta} (within-class minus between-class cosine similarity of sentence embeddings), we show that emojis used in tightly clustered topical contexts also exhibit \emph{more} polarised crowd-sourced sentiment, not less. Validated against the Kralj Novak et al.\ (2015) sentiment lexicon, the central correlation is Spearman $ρ= +0.361$ (Holm $p = 0.015$, 95\% bootstrap CI $[+0.10, +0.59]$); against the independent Ferré et al.\ (2025) Spanish 13-emotion ratings, $ρ= +0.509$ ($p = 0.0003$). The result survives permutation testing, BCa bootstrap, leave-one-out jackknife, partial correlation controlling for emoji frequency, and Holm correction across a four-test family. Topical coherence and affective dispersion are distinct dimensions of emoji meaning. As a downstream check we report leakage-free five-fold cross-validation results for sarcasm classification on the Emoji Sarcasm Dataset; all three baselines (a gated multimodal architecture, fine-tuned BERTweet features, and fine-tuned RoBERTa features) score above 0.97 macro F1, indicating the dataset has saturating emoji-label cues. We frame the classifier as supporting evidence rather than the contribution.

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Topics: Emoji, Sarcasm, Psychology, Natural language processing, Correlation, Sentence

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