# Spice up Your Chat: The Intentions and Sentiment Effects of Using Emoji

arXiv: https://arxiv.org/abs/1703.02860 · HTML: https://mlsanity.com/paper/1703.02860/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2017.

## Built on by (most cited first, 12 of 12)

- [Human vs. LMMs: Exploring the Discrepancy in Emoji Interpretation and Usage in Digital Communication](https://mlsanity.com/paper/2401.08212/) (2024)
- [Emojis Decoded: Leveraging ChatGPT for Enhanced Understanding in Social Media Communications](https://mlsanity.com/paper/2402.01681/) (2024)
- [Emoji Promotes Developer Participation and Issue Resolution on GitHub](https://mlsanity.com/paper/2308.16360/) (2023)
- [Irony in Emojis: A Comparative Study of Human and LLM Interpretation](https://mlsanity.com/paper/2501.11241/) (2025)
- [Improving Social Meaning Detection with Pragmatic Masking and Surrogate Fine-Tuning](https://mlsanity.com/paper/2108.00356/) (2021)
- [When Smiley Turns Hostile: Interpreting How Emojis Trigger LLMs' Toxicity](https://mlsanity.com/paper/2509.11141/) (2025)
- [Emoji-based Co-attention Network for Microblog Sentiment Analysis](https://mlsanity.com/paper/2110.14227/) (2021)
- [Unveiling the Limits of Large Language Models in Inferring Pragmatic Meaning from Non-Verbal Responses](https://mlsanity.com/paper/2606.01845/) (2026)
- [Pay attention to emoji: Feature Fusion Network with EmoGraph2vec Model for Sentiment Analysis](https://mlsanity.com/paper/2110.14636/) (2021)
- [The Hidden Language of Harm: Examining the Role of Emojis in Harmful Online Communication and Content Moderation](https://mlsanity.com/paper/2506.00583/) (2025)
- [Memes-as-Replies: Can Models Select Humorous Manga Panel Responses?](https://mlsanity.com/paper/2602.15842/) (2026)
- [Digital Skin, Digital Bias: Uncovering Tone-Based Biases in LLMs and Emoji Embeddings](https://mlsanity.com/paper/2604.06863/) (2026)

## Abstract (arXiv)

Emojis, as a new way of conveying nonverbal cues, are widely adopted in computer-mediated communications. In this paper, first from a message sender perspective, we focus on people's motives in using four types of emojis -- positive, neutral, negative, and non-facial. We compare the willingness levels of using these emoji types for seven typical intentions that people usually apply nonverbal cues for in communication. The results of extensive statistical hypothesis tests not only report the popularities of the intentions, but also uncover the subtle differences between emoji types in terms of intended uses. Second, from a perspective of message recipients, we further study the sentiment effects of emojis, as well as their duplications, on verbal messages. Different from previous studies in emoji sentiment, we study the sentiments of emojis and their contexts as a whole. The experiment results indicate that the powers of conveying sentiment are different between four emoji types, and the sentiment effects of emojis vary in the contexts of different valences.