DETECTION OF DEPRESSION STATUS FROM TEXT USING NLP APPROACH: COMPARATIVE PERFORMANCE OF DIFFERENT ENSEMBLE ALGORITHMS

Abstract

This study aims to compare the performance of different ensemble learning algorithms using Natural Language Processing (NLP) approach for the detection of depression status from text. Depression is a common mental health problem worldwide and early diagnosis and intervention are of critical importance. A dataset consisting of 124,017 tweets collected between 2019-2020 was used in the study. These tweets were classified according to their depressive and non-depressive content. Five different ensemble learning algorithms, namely Random Forest, AdaBoost, Gradient Boosting, XGBoost and Voting Classifier, were applied in the study. The performance of the models was evaluated using various metrics such as accuracy, precision, recall and F1-score. In addition, ROC curves and learning curves were analyzed. The results revealed that all ensemble learning algorithms showed high performance, but Random Forest gave the best results in all metrics. Voting Classifier performed the second best, while Gradient Boosting performed relatively poorly. This study shows that ensemble learning algorithms are effective in detecting depression from text, and Random Forest in particular can be used as a potential screening tool in this area. The findings may contribute to the development of technology-supported approaches for early diagnosis and intervention in the field of mental health.

Keywords

• Depression Detection
• Natural Language Processing (NLP)
• Ensemble Learning Algorithms
• Social Media Analysis

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