BERT-BiLSTM Architecture to Modelling Depression Recognition for Indonesian Text from English Social Media

Depression is a common mental health disorder. It can greatly affect our daily lives. Depressed people are generally prone to negative emotions. Hence, recognising a sign of depression is an important task. Several techniques are proposed to model automatic depression recognition from several modalities. However, there is a limited number of datasets and research done in a local language (i.e. Indonesian). Expressing thoughts and feelings are unique based on their backgrounds (e.g. race, religion and culture). Hence, fine-tuning the model to a local language or culture is also important. This research aims to build a model using deep learning to recognise depression signs from the text in the local language (i.e. Indonesian). Seven models are proposed in this research to model depression recognition from social media. The result illustrates that combining Bidirectional Long short-term memory with Bidirectional Encoder Representations from Transformers architecture can improve the performance of the model.