2021
IJCNLP
IJCNLP 2021
Assessing Emoji Use in Modern Text Processing Tools
Abstract
AbstractEmojis have become ubiquitous in digital communication, due to their visual appeal as well as their ability to vividly convey human emotion, among other factors. This 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, dependency parsing, as well as sentiment analysis. Our findings show that many systems still have notable shortcomings when operating on text containing emojis.
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio
Authors
Topics
Natural Language Processing > Understanding > Part-of-Speech Tagging
Natural Language Processing > Understanding > Parsing
Natural Language Processing > Understanding > Sentiment Analysis
Natural Language Processing > Resources & Methods > Multilingual NLP
Natural Language Processing > Resources & Methods > Text Representation