# Assessing Emoji Use in Modern Text Processing Tools

arXiv: https://arxiv.org/abs/2101.00430 · HTML: https://mlsanity.com/paper/2101.00430/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 30 references that are in the map.
- First posted on arXiv 2 Jan 2021.

## Builds on (most cited first, 4 of 4)

- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [AllenNLP: A Deep Semantic Natural Language Processing Platform](https://mlsanity.com/paper/1803.07640/) (2018)
- [Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm](https://mlsanity.com/paper/1708.00524/) (2017)
- [Sentiment of Emojis](https://mlsanity.com/paper/1509.07761/) (2015)

## Built on by (most cited first, 1 of 1)

- [Are LLMs Safe Beyond Text: Do Emojis Expose Gaps in Safety Evaluation](https://mlsanity.com/paper/2608.18164/) (2026)

## Abstract (arXiv)

Emojis have become ubiquitous in digital communication, due to their visual appeal as well as their ability to vividly convey human emotion, among other factors. The growing prominence of emojis in social media and other instant messaging also leads to an increased need for systems and tools to operate on text containing emojis. In this study, we assess this support by considering test sets of tweets with emojis, based on which we perform a series of experiments investigating the ability of prominent NLP and text processing tools to adequately process them. In particular, we consider tokenization, part-of-speech tagging, as well as sentiment analysis. Our findings show that many tools still have notable shortcomings when operating on text containing emojis.