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Analyzing Gender Polarity in Short Social Media Texts with BERT: The Role of Emojis and Emoticons

arXiv 2406.09573 ↗First posted 13 Jun 2024cs.CL cs.AIcited by 0

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Abstract (arXiv)

In this effort we fine tuned different models based on BERT to detect the gender polarity of twitter accounts. We specially focused on analyzing the effect of using emojis and emoticons in performance of our model in classifying task. We were able to demonstrate that the use of these none word inputs alongside the mention of other accounts in a short text format like tweet has an impact in detecting the account holder's gender.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 2 of its 9 references that are in the map. First posted on arXiv 13 Jun 2024.

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2 of 9 references.

References · 2

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2022Twitter-Based Gender Recognition Using Transformerscited by 1

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