What is it about?
To develop a reliable automatic depression detection system, the social media text cannot be used directly as there is a lot of irrelevant, inaccurate, and noisy information available. Moreover, the basic preprocessing steps which are used with most of the machine learning models have limited functionality and thus lead to lots of information loss. This loss of information is not affordable especially in the domain of affective computing (mental health) for text. In this paper, we present various preprocessing techniques for depressive text, DPre, to obtain readable text from raw and noisy tweets. This method can help in minimizing the loss of information and expressions hidden in the raw tweet. Moreover, the processed and clean text will be ready to input into any machine learning algorithm.
Why is it important?
This method can help in minimizing the loss of information and expressions hidden in the raw tweet. Moreover, the processed and clean text will be ready to input into any machine learning algorithm.