Email this article Health Monitoring for Overtime Usage of Social Media
Abstract
We are keen on utilizing online life to screen individuals' wellbeing after some time. The utilization of tweets has a few advantages including prompt information accessibility at practically no expense. Early checking of wellbeing information is correlative to post-factum thinks about and empowers a scope of utilizations, for example, estimating conduct hazard factors and activating wellbeing efforts. We detail two issues: wellbeing progress recognition and wellbeing change expectation. We initially propose the Temporal Ailment Topic Aspect Model (TM–ATAM), another idle model devoted to taking care of the main issue by catching advances that include wellbeing related points. TM–ATAM is a non-clear expansion to ATAM that was intended to remove wellbeing related themes. It learns wellbeing related point advances by limiting the forecast blunder on theme appropriations between continuous posts at various time and geographic granularities. To take care of the subsequent issue, we create T–ATAM, a Temporal Ailment Topic Aspect Model where time is treated as an irregular variable locally inside ATAM
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