Gender Classification of Twitter Users Using Convolutional Neural Network
Abstract
Social media has become a place for social media analysts to obtain data to gain deeper insights and understanding of user behavior, trends, public opinion, and patterns associated with social media usage. Twitter is one of the most popular social media platforms where users can share messages or ”tweets” in a short text format. However, on Twitter, user information such as gender is not shown, but without realizing it or not, there is information about it in an unstructured manner. In social media analytics, gender is one of the important data that someone likes, so this research was conducted to determine the best accuracy for gender classification. The purpose of this study was to determine whether using combined data can improve the accuracy of gender classification using data from Twitter, tweets, and descriptions. The method used was word vector representation using word2vec and the application of a 2D Convolutional Neural Network (CNN) model. Word2vec was used to generate word vector representations that take into account the context and meaning of words in the text. The 2D CNN model extracted features from the word vector representation and performed gender classification. The research aimed to compare tweet data, descriptions, and a combination of tweets and descriptions to find the most accurate. The result of this study was that combined data between tweets and
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