Tracing the Emotional Roadmap of Depressive Users on Social Media Through Sequential Pattern Mining
نویسندگان
چکیده
Depression is one of the most growing health disorders, generating social and economic problems. The affective computing models focus on analyzing unique user posts, not observing temporal behavior patterns, which are essential to track changes evolution emotional context, that involves persistent analysis feelings characteristics over time. This article proposes TROAD framework for longitudinal recognition sequential patterns from depressive users media. identifies best interval analyze every activity, extracts contextual features data, into time windows recognize behavior. main found in top-10 rules negative emotions: violence, pain, shame, depression, sadness, silence. We obtained strong sequence with a minimum 70% support, 81% confidence, 69% regarding considering periods silence between users' posts. Without silent periods, showed 70%, 86%, 38% confidence. computational approach promising tool clinical specialists human
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2021
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2021.3095759