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Geetha, G., Saranya, G., Chakrapani, K., Godwin Ponsam, J., Safa, M. and Karpagaselvi, S. (2020) Early Detection of Depression from Social Media Data Using Machine Learning Algorithms. 2020 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), Chennai, 10-11 December 2020, 1-6.
https://doi.org/10.1109/ICPECTS49113.2020.9336974
has been cited by the following article:
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TITLE:
A Stacking-Based Ensemble Approach with Embeddings from Language Models for Depression Detection from Social Media Text
AUTHORS:
Akwa Gaius, Ronald Waweru Mwangi, Antony Ngunyi
KEYWORDS:
Machine Learning, Natural Language Processing, Depression
JOURNAL NAME:
Journal of Data Analysis and Information Processing,
Vol.11 No.4,
November
10,
2023
ABSTRACT: Depression is a major public health problem around the world and
contributes significantly to poor health and poverty. The rate of the number of
people being affected is very high compared to the rate of medical treatment of
the disease. Thus, the disease often remains untreated and suffering continues.
Machine learning has been widely used in many studies in detecting depressive
individuals from their contents on online social networks. From the related
reviews, it is apparent that the application of stacking for diagnosing
depression has been minimal. The study implements stacking based on Extra Tree,
Extreme Gradient Boosting, Light Gradient Boosting and Multi-layer perceptron
and compares its performance to state of the art bagging and boosting ensemble
learners. To better evaluate the effectiveness of the proposed stacking
approach, three pretrain word embeddings techniques including: Word2vec, Global
Vectors and Embeddings from language models were employed with two datasets.
Also, a corrected resampled paired t-test was applied to test the significance
of the stacked accuracy against the baseline accuracy. The experimental results
shows that the stacking approach yields favourable results with a best accuracy
of 99.54%.