A Novel Approach to Predicting Personality Behaviour from Social Media Data Using Deep Learning
Keywords:
Social Media, Deep Learning, Predictive Analytics, Personal Life BehaviourAbstract
In the era of prolific social media engagement, understanding and predicting personal life behaviour play a pivotal role in tailoring user experiences and providing targeted services. This paper introduces a cutting-edge approach leveraging deep learning techniques to predict personal life behaviour from social media data. The proposed methodology goes beyond traditional analyses by harnessing the power of deep learning, specifically recurrent neural networks (RNNs), to discern intricate patterns within users' online activities. By training on vast datasets of opinions, sentiments, and personal activities shared on social platforms, the model establishes a nuanced understanding of individual behaviour. The study addresses the inherent challenges of capturing the dynamic nature of personal life behaviour and explores the potential of recurrent neural networks in forecasting future behaviours. To validate the efficacy of the proposed approach, comprehensive evaluations are conducted using real-world social media datasets. The results not only demonstrate the model's ability to predict personal life behaviour accurately but also shed light on the interpretability and generalizability of the deep learning framework in this context. This research contributes to advancing the frontier of predictive analytics in social media, offering valuable insights for personalized user interactions and targeted services.
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