Detecting Depression in Social Media : An Emotional Analysis Approach

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Abstract
Depression has been an ongoing mental health issue that has been affecting a wide range of humanity, particularly the young adults. To address and observe the more general public in a natural habitat, social media is examined for constructing a system to accurately detect depression. Despite the assiduous effort to construct a novel mechanism to detect depression from social media, behavioral approaches had underlying problems for users with a short activity span. To address this problem, emotion analysis was used as a tool to extract the emotion(s) of a user’s post to identify those with depression. Via machine learning techniques to construct an emotion classifier which in turn creates emotion embeddings for a binary classifier, this study proposes a pipeline structure to identify reddit posts from the depression subreddit. The model yielded promising results, introducing emotional analysis as a novel methodology in assessing mental health within social media.
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2019-05-30
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depression; Computer science; Social Media; Emotional Analysis
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Cardie, Claire T.
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Bunea, Florentina
Degree Discipline
Computer Science
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M.S., Computer Science
Degree Level
Master of Science
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Government Document
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dissertation or thesis
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