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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

Chinese

我們認為這幅畫面是不完整的。 從 16{,}575 英國 Tweets 和每個 Emoji 指標,我們稱之為 \emph{stability delta} (在階級內減少句子嵌入的單一類似性),我們表明,在緊密集合的當地情境中使用的 Emoji 也顯示 \emph{more} 極化群眾來源的情緒,而不是更少。 與 Kralj Novak et al.\ (2015) 情緒詞語相對,中央相對性是Spearman $ρ=0.361$(Holm $p = 0.015$,95% bootstrap CI $[+0.10, +0.59]); 對於獨立的Ferré et al. et al. 的分類,我們提供西班牙情緒評級, $

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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