Spice up Your Chat: The Intentions and Sentiment Effects of Using Emoji
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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.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Mar 2017.
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Citing papers per year: 2021: 3; 2022: 0; 2023: 1; 2024: 2; 2025: 3; 2026: 3.
12 papers.
- 2024Human vs. LMMs: Exploring the Discrepancy in Emoji Interpretation and Usage in Digital Communicationcited by 8
- 2024Emojis Decoded: Leveraging ChatGPT for Enhanced Understanding in Social Media Communicationscited by 7
- 2023Emoji Promotes Developer Participation and Issue Resolution on GitHubcited by 6
- 2021Improving Social Meaning Detection with Pragmatic Masking and Surrogate Fine-Tuningcited by 5
- 2025Irony in Emojis: A Comparative Study of Human and LLM Interpretationcited by 5
- 2025When Smiley Turns Hostile: Interpreting How Emojis Trigger LLMs' Toxicitycited by 2
- 2021Emoji-based Co-attention Network for Microblog Sentiment Analysiscited by 1
- 2026Unveiling the Limits of Large Language Models in Inferring Pragmatic Meaning from Non-Verbal Responsescited by 1
- 2021Pay attention to emoji: Feature Fusion Network with EmoGraph2vec Model for Sentiment Analysiscited by 0
- 2025The Hidden Language of Harm: Examining the Role of Emojis in Harmful Online Communication and Content Moderationcited by 0
- 2026Memes-as-Replies: Can Models Select Humorous Manga Panel Responses?cited by 0
- 2026Digital Skin, Digital Bias: Uncovering Tone-Based Biases in LLMs and Emoji Embeddingscited by 0
- 2026Unveiling the Limits of Large Language Models in Inferring Pragmatic Meaning from Non-Verbal Responses1 Jun 2026
- 2026Digital Skin, Digital Bias: Uncovering Tone-Based Biases in LLMs and Emoji Embeddings8 Apr 2026
- 2026Memes-as-Replies: Can Models Select Humorous Manga Panel Responses?21 Jan 2026
- 2025When Smiley Turns Hostile: Interpreting How Emojis Trigger LLMs' Toxicity14 Sep 2025
- 2025The Hidden Language of Harm: Examining the Role of Emojis in Harmful Online Communication and Content Moderation31 May 2025
- 2025Irony in Emojis: A Comparative Study of Human and LLM Interpretation20 Jan 2025
- 2024Emojis Decoded: Leveraging ChatGPT for Enhanced Understanding in Social Media Communications22 Jan 2024
- 2024Human vs. LMMs: Exploring the Discrepancy in Emoji Interpretation and Usage in Digital Communication16 Jan 2024
- 2023Emoji Promotes Developer Participation and Issue Resolution on GitHub30 Aug 2023
- 2021Pay attention to emoji: Feature Fusion Network with EmoGraph2vec Model for Sentiment Analysis27 Oct 2021
- 2021Emoji-based Co-attention Network for Microblog Sentiment Analysis27 Oct 2021
- 2021Improving Social Meaning Detection with Pragmatic Masking and Surrogate Fine-Tuning1 Aug 2021