Social networking and other content sharing services are becoming increasingly popular throughout the world. The ability to identify a person's gender based on these brief communications is an intriguing research topic that has applications in forensics, marketing analysis, advertising, and recommendation. The usage of tweets and Natural Language Processing (NLP) methods in a gender classification system will be investigated in this study. This study will look into how a system for classifying people by their gender uses tweets and Natural Language Processing (NLP) techniques. For the sake of this study, a brand-new dataset that includes the user gender and related tweets has been created. This dataset was created since there was no publicly accessible standard dataset with the volume needed to carry out this inquiry. According to the findings, the conventional Bag of Words model did not produce any noteworthy categorization outcomes. However, using a variety of machine learning techniques, word embedding models have greatly outperformed them. As a result, it has been demonstrated that word embedding models are the best method for identifying gender from text data from Twitter.
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