Summary of Analyzing Gender Polarity in Short Social Media Texts with Bert: the Role Of Emojis and Emoticons, by Saba Yousefian Jazi et al.
Analyzing Gender Polarity in Short Social Media Texts with BERT: The Role of Emojis and Emoticons
by Saba Yousefian Jazi, Amir Mirzaeinia, Sina Yousefian Jazi
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the application of BERT-based models in identifying the gender polarity of Twitter accounts, with a specific focus on the impact of using emojis and emoticons. By analyzing the effect of incorporating these non-verbal inputs alongside mentions of other accounts in short text formats like tweets, the study demonstrates that their inclusion improves model performance in classifying task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research shows how we can use special models based on BERT to figure out if a Twitter account is run by a man or woman. The study looked at how using emojis and smiley faces helps our model make better decisions about gender identity. By studying how these non-verbal clues work together with mentions of other accounts, we learned that they make our model more accurate. |
Keywords
» Artificial intelligence » Bert
