2023
DOI: 10.32604/iasc.2023.034623
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Gender Identification Using Marginalised Stacked Denoising Autoencoders on Twitter Data

Abstract: Gender analysis of Twitter could reveal significant socio-cultural differences between female and male users. Efforts had been made to analyze and automatically infer gender formerly for more commonly spoken languages' content, but, as we now know that limited work is being undertaken for Arabic. Most of the research works are done mainly for English and least amount of effort for non-English language. The study for Arabic demographic inference like gender is relatively uncommon for social networking users, es… Show more

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