A Systematic Analysis of Text Classification Overfitting Recommendation Methods
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Abstract
With increased mental health awareness, identifying mental illness is becoming a significant problem. Due to the complexity of mental ailments, many psychiatrists find it impossible to diagnose and cure patients before it is too late. However, the everyday use of social media creates an atmosphere that may reveal extra information about a patient’s mental illness. This study conducts a Systematic Literature Review (SLR) to address research questions. Some aspects of how depression decision-making is detected using different datasets covering social media, surveys, and medical bio-markers. However, most studies employ Machine learning and deep learning models like RNN to predict depression decision-making due to the lack of data. This study will try to identify a more effective way of identifying the overfitting solution during text-based training and learning while discussing various depression detection algorithms.
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