# Sentiment of Emojis

arXiv: https://arxiv.org/abs/1509.07761 · HTML: https://mlsanity.com/paper/1509.07761/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2015.

## Built on by (most cited first, 30 of 32)

- [NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis](https://mlsanity.com/paper/2201.08277/) (2022)
- [What Do VLMs NOTICE? A Mechanistic Interpretability Pipeline for Gaussian-Noise-free Text-Image Corruption and Evaluation](https://mlsanity.com/paper/2406.16320/) (2024)
- [Human vs. LMMs: Exploring the Discrepancy in Emoji Interpretation and Usage in Digital Communication](https://mlsanity.com/paper/2401.08212/) (2024)
- [Semantic Journeys: Quantifying Change in Emoji Meaning from 2012-2018](https://mlsanity.com/paper/2105.00846/) (2021)
- [When and Why does a Model Fail? A Human-in-the-loop Error Detection Framework for Sentiment Analysis](https://mlsanity.com/paper/2106.00954/) (2021)
- [On the Context-Free Ambiguity of Emoji](https://mlsanity.com/paper/2201.06302/) (2022)
- [TurkishBERTweet: Fast and Reliable Large Language Model for Social Media Analysis](https://mlsanity.com/paper/2311.18063/) (2023)
- [A `Sourceful' Twist: Emoji Prediction Based on Sentiment, Hashtags and Application Source](https://mlsanity.com/paper/2103.07833/) (2021)
- [Follow Us and Become Famous! Insights and Guidelines From Instagram Engagement Mechanisms](https://mlsanity.com/paper/2301.06815/) (2023)
- [Tracking Emotional Dynamics in Chat Conversations: A Hybrid Approach using DistilBERT and Emoji Sentiment Analysis](https://mlsanity.com/paper/2408.01838/) (2024)
- [Does Machine "know" interpersonal pragmatics? Evidence from MARBERT's learning of emoji pragmatics in Arabic digital discourse](https://mlsanity.com/paper/2608.01174/) (2026)
- [Assessing Emoji Use in Modern Text Processing Tools](https://mlsanity.com/paper/2101.00430/) (2021)
- [Creating emoji lexica from unsupervised sentiment analysis of their descriptions](https://mlsanity.com/paper/2404.01439/) (2024)
- [A Multimodal Framework for Depression Detection during Covid-19 via Harvesting Social Media: A Novel Dataset and Method](https://mlsanity.com/paper/2511.00424/) (2025)
- [False Friends in the Shell: Unveiling the Emoticon Semantic Confusion in Large Language Models](https://mlsanity.com/paper/2601.07885/) (2026)
- [SentEmojiBot: Empathising Conversations Generation with Emojis](https://mlsanity.com/paper/2105.12399/) (2021)
- [FeelsGoodMan: Inferring Semantics of Twitch Neologisms](https://mlsanity.com/paper/2108.08411/) (2021)
- [Emoji-based Fine-grained Attention Network for Sentiment Analysis in the Microblog Comments](https://mlsanity.com/paper/2206.12262/) (2022)
- [LIP: Lightweight Intelligent Preprocessor for meaningful text-to-speech](https://mlsanity.com/paper/2207.07118/) (2022)
- [emojiSpace: Spatial Representation of Emojis](https://mlsanity.com/paper/2209.09871/) (2022)
- [XAI in Computational Linguistics: Understanding Political Leanings in the Slovenian Parliament](https://mlsanity.com/paper/2305.04631/) (2023)
- [Detection of depression on social networks using transformers and ensembles](https://mlsanity.com/paper/2305.05325/) (2023)
- [Emotion Embeddings unicodex2014 Learning Stable and Homogeneous Abstractions from Heterogeneous Affective Datasets](https://mlsanity.com/paper/2308.07871/) (2023)
- [Vulgar Remarks Detection in Chittagonian Dialect of Bangla](https://mlsanity.com/paper/2308.15448/) (2023)
- [Perceiving University Student's Opinions from Google App Reviews](https://mlsanity.com/paper/2312.06705/) (2023)
- [Unlocking Cross-Lingual Sentiment Analysis through Emoji Interpretation: A Multimodal Generative AI Approach](https://mlsanity.com/paper/2412.17255/) (2024)
- [Quantifying Feature Importance for Online Content Moderation](https://mlsanity.com/paper/2510.19882/) (2025)
- [Language-Independent Sentiment Labelling with Distant Supervision: A Case Study for English, Sepedi and Setswana](https://mlsanity.com/paper/2511.19818/) (2025)
- [Emoji-Based Jailbreaking of Large Language Models](https://mlsanity.com/paper/2601.00936/) (2026)
- [Digital Skin, Digital Bias: Uncovering Tone-Based Biases in LLMs and Emoji Embeddings](https://mlsanity.com/paper/2604.06863/) (2026)

## Abstract (arXiv)

There is a new generation of emoticons, called emojis, that is increasingly being used in mobile communications and social media. In the past two years, over ten billion emojis were used on Twitter. Emojis are Unicode graphic symbols, used as a shorthand to express concepts and ideas. In contrast to the small number of well-known emoticons that carry clear emotional contents, there are hundreds of emojis. But what are their emotional contents? We provide the first emoji sentiment lexicon, called the Emoji Sentiment Ranking, and draw a sentiment map of the 751 most frequently used emojis. The sentiment of the emojis is computed from the sentiment of the tweets in which they occur. We engaged 83 human annotators to label over 1.6 million tweets in 13 European languages by the sentiment polarity (negative, neutral, or positive). About 4% of the annotated tweets contain emojis. The sentiment analysis of the emojis allows us to draw several interesting conclusions. It turns out that most of the emojis are positive, especially the most popular ones. The sentiment distribution of the tweets with and without emojis is significantly different. The inter-annotator agreement on the tweets with emojis is higher. Emojis tend to occur at the end of the tweets, and their sentiment polarity increases with the distance. We observe no significant differences in the emoji rankings between the 13 languages and the Emoji Sentiment Ranking. Consequently, we propose our Emoji Sentiment Ranking as a European language-independent resource for automated sentiment analysis. Finally, the paper provides a formalization of sentiment and a novel visualization in the form of a sentiment bar.