Chapter Ten: Improving mental health surveillance over Twitter text classification using word embedding techniques
Reno Ardian Syaputra, and Rashid Ali
Department of Computer Engineering, Aligarh Muslim University, Aligarh, Uttar Pradesh, India
Abstract
Web users are progressively connecting during the pandemic of Covid-19. It causes the social web to grow exponentially by the huge amount of collective information. For example, Twitter, which has been growing very fast as one of the most popular social networking websites. The platform enables tracking mental health surveillance via online by using text classification methods. Latest text classification research showed that tweets can be classified accurately by using word embedding ...
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