Analyzing the Combined Effects of Sarcasm and Emotion For Gender Prediction
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Abstract
“Women are bitchy but men are sarcastic”, such comments reveal the relationship between gender and sarcasm. Automatic gender identification can play a crucial role in services that depend on data about a user’s background. Although for some social media users the gender of a user is typically unavailable due to privacy and anonymity. Based on the notion that male and female users may express their thoughts and sentiments differently in their posts, social media accounts can be examined using their posts (text) in order to automatically identify the gender of an anonymous user. In the current work, efforts are made in analyzing the effects of emotion and sarcasm intended by the users in their tweets for predicting gender. Sarcasm + emotion aided gender prediction systems are developed using different machine learning and neural network-based architectures. In our developed model, tweet features are extracted by using pre-trained GloVe embeddings. The sarcasm intensity is concatenated with the corresponding tweet representation and at last classification layer is used to predict the gender labels. For the experimentation purpose, the PAN-2018 dataset has been used. We have also shown the effect of utilizing emotion, and sarcasm information over gender prediction using different models.
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