Abstract
Depression is a serious mental illness that leads to social disengagement, affects an individual’s professional and personal life. Several studies and research programs are conducted for understanding the main causes of depression and an indication of psychological problems through speech and text-based data generated by human beings. The language is considered to be directly related to the current mental state of an individual, that’s why social media network is utilized by researchers in detecting depression and helps in the implementation of the intervention program. We proposed a hybrid model using CNN & LSTM models for detecting depressed individuals through normal conversation-based text data, that retrieved from twitter. We employ machine-learning classifiers and proposed method on twitter dataset to compare their performance for depression detection. The proposed model provides an accuracy of 92% in comparison with the machine learning technique that gives a maximum accuracy of 83%.
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Verma, B., Gupta, S., Goel, L. (2020). A Neural Network Based Hybrid Model for Depression Detection in Twitter. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T., Valentino, G. (eds) Advances in Computing and Data Sciences. ICACDS 2020. Communications in Computer and Information Science, vol 1244. Springer, Singapore. https://doi.org/10.1007/978-981-15-6634-9_16
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DOI: https://doi.org/10.1007/978-981-15-6634-9_16
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