Research Area: Machine Learning
Depression problems can severely affect not only personal health, but also society. There is evidence that shows people who suffer from depression problems tend to express their feelings and seek help via online posts on online platforms. This study is conducted to apply Natural Language Processing (NLP) with messages associated with depression problems. Feature extractions, machine learning, and neural network models are applied to carry out the detection. The CNN-LSTM model, a unified model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory Networks (LSTM), is used sequentially and in parallel as branches to compare the outcomes with baseline models. In addition, different types of activation functions are applied in the CNN layer to compare the results. In this study, the CNN-LSTM models show improvement over the classical machine learning method. However, there is a slight improvement among the CNN-LSTM models. The three-branch CNN-LSTM model with the Rectif ied Linear Unit (ReLU) activation function is capable of achieving the F1-score of 83.1%.
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Author(s) Name: Boriharn Kumnunt and Ohm Sornil
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Conferrence name: In Proceedings of the 1st International Conference on Deep Learning Theory and Applications - DeLTA
Publisher name: SCITEPRESS
DOI: 10.5220/0009970501110118
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