Analyzing Gender Polarity in Short Social Media Texts with BERT: The Role of Emojis and Emoticons
This addresses gender detection in social media for researchers, but is incremental as it builds on existing BERT methods with minor feature additions.
The study fine-tuned BERT models to detect gender polarity from Twitter accounts, finding that incorporating emojis, emoticons, and account mentions in short texts improves classification performance.
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.