Emoji-Emotion Ranking System Using Twitter Data

Danila Khlebokazov; Nurkhan Tashimov; Pakizar Shamoi

cs.CLOct 3, 2026

Emoji-Emotion Ranking System Using Twitter Data

Authors: Danila Khlebokazov, Nurkhan Tashimov, Pakizar Shamoi

Organizations: School of Information Technology and Engineering, KBTU Almaty, Kazakhstan

Abstract

Nowadays, emojis are often replacing words. Yet computational systems still oversimplify them. Most existing approaches treat emojis as static sentiment indicators and overlook their emotional distributions. In this study, we propose an emoji-aware emotion analysis framework based on a Twitter (X) dataset of 100,000 emoji-containing replies collected between 2020-2025. After preprocessing and text cleaning, we applied text-to-emotion classification to detect five primary emotions (Happy, Angry, Sad, Fear, and Surprise) for each message. By aggregating emotion scores across contexts in which each emoji appears, we estimate emoji-emotion association distributions and construct an emoji-emotion ranking system reflecting relative emotional dominance. Furthermore, we project emojis into the Russell valence-arousal space to enable continuous affective interpretation. Our results demonstrate that emojis exhibit probabilistic, context-sensitive emotional profiles rather than fixed sentiment polarities.

Figures & tables

Fig. 1 : Samples from a collected Twitter (X) dataset.

Field	Count	Share (%)
Total posts	100,000	100.0
With $\geq$ 2 emojis	14,646	14.6
Avg. tokens / emojis	11.94 / 1.29	–

TABLE I : Descriptive statistics of the dataset

Fig. 2 : Russell Circumplex Model of Affect illustrating the two-dimensional structure of emotions defined by Valence (horizontal axis) and Arousal (vertical axis).

Fig. 3 : High-level view of the proposed approach

Emoji	Count
	$47\,059$
	$40\,932$
	$34\,494$
	$31\,543$
	$29\,359$
	$25\,216$

TABLE II : Top 20 most frequent emojis

Emoji 1	Emoji 2	Count	Probability
		129	0.8836
		418	0.7902
		157	0.7850
		250	0.7485
		143	0.7333
		145	0.7250

TABLE III : Top 20 most probable combination

Expected	Actual	Exp. Intensity	Act. Intensity
Happy	Fear	0.2039	0.2978
Happy	Fear	0.1978	0.2999
Sad	Fear	0.2442	0.2651
Happy	Fear	0.2425	0.2609
Angry	Fear	0.1172	0.2966
Surprise	Fear	0.2698	0.2719

TABLE IV : Unexpected Emotions

Emoji 1	Emoji 2	Count	Probability
		1364	0.4219
		723	0.7081
		633	0.6200
		593	0.6095
		521	0.1344
		517	0.6949

TABLE V : Top 20 emojis by co-occurrence

Fig. 7 : Emoji positions in the Russell circumplex (Valence on x, Arousal on y). Points are sized by frequency and colored by top emotion.

Emotion Recognition

Emotion recognition uses language, voice, face, body movement, and physiological signals to infer emotional categories or dimensions such as valence and arousal.

See all 129 papers

Beyond a Single Sentiment Score: Topical and Affective Dimensions of Emoji Meaning on Twitter
Oct 20, 2026Rohan James, Ioannis Christofilogiannis, Yuuv Jauhari +2Social Media Analysis Sentiment Analysis
School of Computer Science and Statistics Trinity College Dublin, Ireland
Machine learning and digital pragmatics: Which word category influences emoji use most?
Aug 22, 2026Mohammed Q. Shormani, Yehia A. AlSohbani
Ibb University, Ibb, Yemen · Arab Open University, Riyadh, Saudi Arabia
Two Emojis of Difference: What Multilingual Affective Generation Benchmarks Actually Measure
Sep 24, 2026Fardeen Sadab, Adib SakhawatMultilingual Language Model Evaluation Language Model Generation Evaluation
Department of Computer Science and Engineering Islamic University of Technology, Dhaka, Bangladesh

Oct 20, 2026J/K move · Enter open · S save

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

Rohan James, Ioannis Christofilogiannis, Yuuv Jauhari, Shreya Shah+1

School of Computer Science and Statistics Trinity College Dublin, Ireland

Social Media Analysis Sentiment Analysis

Open paper AskPDF arXiv

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 stability delta (within-class minus between-class cosine similarity of sentence embeddings), we show that emojis used in tightly clustered topical contexts also exhibit 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.

Abstract

Emotion Recognition

Explore similar work

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

Machine learning and digital pragmatics: Which word category influences emoji use most?

Two Emojis of Difference: What Multilingual Affective Generation Benchmarks Actually Measure