Unlocking Author Gender Clues: Machine Learning and Pronoun Insights for Gender Prediction in Bangla Literature

Gender identification has emerged as a crucial research domain, with diverse applications in social media analysis, forensic linguistics, and author profiling. However, this task is complicated by the constraints on text length available for analysis. In this study, we introduce a novel method for predicting the gender of Bangla authors by examining their written works. We compiled a distinct dataset that includes texts from 48 authors (35 male and 13 female). Four machine learning models were evaluated: Support Vector Machine, Naive Bayes, Decision Tree, and Random Forest. Among these, the Random Forest model achieved the highest accuracy, reaching $92 \%$. The analysis identified 20 significant Bangla pronouns, with notable ones such as “এ” (E), “ও” (O), and “আপিন” (Apni) serving as strong gender indicators.