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All Journal International Journal of Electrical and Computer Engineering
Ayyalasomayajula, Madan Mohan Tito
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Computer Science & IT Electrical & Electronics Engineering

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Reddit social media text analysis for depression prediction: using logistic regression with enhanced term frequency-inverse document frequency features Ayyalasomayajula, Madan Mohan Tito; Agarwal, Akshay; Khan, Shahnawaz
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5998-6005

Abstract

Language provides significant insights into an individual’s emotional state, social status, and personality traits. This research aims to enhance depression detection through the analysis of linguistic features and various dataset attributes. The dataset, sourced from the social networking platform Reddit, comprises posts and comments from individuals diagnosed with depression. Logistic regression with term frequency-inverse document frequency (TF-IDF) is employed as the primary model for text classification. To improve model performance, a novel feature—the average time interval between consecutive posts or comments—is introduced, contributing to a marginal but noteworthy improvement in accuracy. The proposed model demonstrates superior F1 scores compared to other models applied to the same dataset. Given the increasing recognition of mental health’s significance, accurately diagnosing mental disorders is of paramount importance. This study underscores the potential of leveraging linguistic analysis and advanced machine learning techniques to identify depressive symptoms, thereby contributing to more effective mental health diagnostics and interventions.
Co-Authors Agarwal, Akshay Khan, Shahnawaz