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An Efficient Identification of Mental Stress by Utilizing Social Network


Srinivasan P , Muthayammal Engineering College; Anitha K, Muthayammal Engineering College; Arul Manikandan S, Muthayammal Engineering College; Jagadeesh Kumar R, Muthayammal Engineering College


Social Mental Stress, NLP, Machine Learning, Security and Sentiment Analysis


The growth of social network leads to more advantages as well as problematic issues. Now days the symptoms and result of this metal stress was analyzed passively and delayed clinical intervention. To overcome this issue, an early stage of identifying online social behavior through analysis in efficient way is proposed. It is challenging to detect SNMSs because the mental status cannot be directly observed from online social activity logs. Through social networks n number of users is communicating in the form of text can’t be analyzed manually to identify the stage of particular user. Manual way of analyzing user chat history will leads to privacy issues therefore this issue has been overcome through Machine Learning (ML). In ML, Natural language processing (NLP) is implemented that analysis the text commented and understand the meaning of the comment. By this analysis a system will identify whether the person is in normal, good or bad (depression) situation without affecting their privacy and confidentiality of data. Therefore, our system will read each word in a command and analysis its meaning and monitor particular person for a specific period and identify whether the person is in good or in depression situation. The person situation is not good then intimation will be send to respective person’s relative mail hence this leads to saving a person life from unexpected event occurrence.

Other Details

Paper ID: IJSRDV8I10322
Published in: Volume : 8, Issue : 1
Publication Date: 01/04/2020
Page(s): 110-113

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