Aim: To automatically identify SNMD patients at the early stage according to their OSN data with a novel tensor model that efficiently integrate heterogeneous data from different OSNs. Existing System: The SNMD data from different OSNs may be incomplete due to the heterogeneity (drawback) . For example, the profiles of users may be empty due to the privacy issue, different functions on different OSNs (e.g., game, check-in, event), etc. Proposed System : propose a machine learning framework, namely, Social Network Mental Disorder Detection (SNMDD) , that exploits features extracted from social network data to accurately identify potential cases of SNMDs. The paper also exploits multi-source learning in SNMDD and propose a new SNMD-based Tensor Model (STM) to improve the accuracy. Advantage: propose a novel tensor-based approach to address the issues of using heterogeneous data and incorporate domain knowledge in SNMD detection. Objectives: · ...
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