Detecting Depression Using Twitter Data by Incorporating Hybrid Feature Representation: A Comparative Machine Learning Approach

Parveen Kumari; Alpana Jijja

doi:10.57159/jcmm.4.6.25240

Authors

Parveen Kumari Sushant University, Gurugram, Haryana, India 122003
Alpana Jijja Sushant University, Gurugram, Haryana, India 122003

DOI:

https://doi.org/10.57159/jcmm.4.6.25240

Keywords:

NLP, Depression Detection, Logistic Regression, Social Media, Text Analytics

Abstract

Depression is a critical global mental health challenge that often remains undiagnosed due to the limitations and subjectivity of conventional screening techniques. The growing use of social media platforms offers new avenues for understanding human emotions, as individuals increasingly share their thoughts, moods, and experiences online. Leveraging this vast digital footprint, the present study introduces a machine learning (ML)-driven approach for the automated detection of depression using Twitter data. A comprehensive dataset comprising 205,271 posts was collected and carefully preprocessed through multiple natural language processing (NLP) techniques, including tokenization, stop-word elimination, lemmatization, and sentiment polarity assessment, to extract meaningful textual features. Six distinct ML models were trained and evaluated: Support Vector Classifier (SVM), Logistic Regression, Decision Tree, AdaBoost, Na"{i}ve Bayes, and K-Nearest Neighbors (KNN). Various performance metrics, including accuracy, precision, recall, and F1-score, were employed to assess the efficiency of each developed model. Among the tested models, Logistic Regression achieved the highest accuracy (92%), followed by SVM with 90%, while KNN performed comparatively lower with 70%. The results indicate that linear and ensemble-based classifiers are more effective than distance-based models in managing high-dimensional text data. Overall, this study offers a robust comparative evaluation of ML algorithms for depression detection and underscores the transformative potential of NLP and social media analytics in scalable, data-driven mental health monitoring systems.

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