Emojis Aid Social Media Sentiment Analysis: Stop Cleaning Them Out!
Author:Murphy | View: 21250 | Time: 2025-03-23 19:58:07
TL;DR:
- Including emojis in the social media sentiment analysis would robustly improve the sentiment classification accuracy no matter what model you use or how you incorporate emojis in the loop
- More than half of the popular BERT-based encoders don't support emojis
- Twitter-RoBERTa encoder performs the best in sentiment analysis and coordinates well with emojis
- Instead of cleaning emojis out, converting them to their textual description can help boost sentiment classification accuracy and handle the out-of-vocabulary issue.
As social media has become an essential part of people's lives, the content that people share on the Internet is highly valuable to many parties. Many modern natural language processing (NLP) techniques were deployed to understand the general public's social media posts. Sentiment Analysis is one of the most popular and critical NLP topics that focuses on analyzing opinions, sentiments, emotions, or attitudes toward entities in written texts computationally [1]. Social media sentiment analysis (SMSA) is thus a field of understanding and learning representations for the sentiments expressed in short social media posts.
Another important feature of this project is the cute little in-text graphics – emojis
