Mohammed MAHMOOD ALI, Mohd TAJUDDIN, M. KABEER

SDF: psychological Stress Detection Framework from Microblogs using Pre-defined rules and Ontologies

Spreading of Unwanted microblogs from Social Networking Sites (SNS) is pervasive in social media that leads to unaccountable disturbances such as Mental disorders, Wastage of precious time, Break-up of relationships, Stressness giving birth to psychological health problems and many more. To overcome these problems, the immense necessity is to ignore those unwanted microblogs in SNS, which is uncontrollable by humans due to addiction towards social media. Even the literate people fall prey to psychological stress from SNS. This seriousness of stress related issues is very rarely attended by researchers, to tackle such vicious microblogs. The prediction strategy is proposed named as Stress Detection Framework (SDF) to analyze the stress in microblog. SDF is developed using Ontology based Information Extraction technique using Probabilistic Model (GSHL & TreeAlignment Algorithm), set of pre-defined knowledge based logical rules that constitutes of low-level attributes (simple textual, linguistic words) and visual features (emoticons & Images) and social Interaction (Likes and Dislikes) to detect and predict stress in microblog messages.SDF is compared with TeniStrength that has shown an increase of 94.2% of stress detection rate. The experimental results obtained will aid to take precise decision for blocking/eradicating/ segregating stress related microblogs from Social media (especially SNS).

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