Detecting Stress Based on Social Networking Interactions
P.C.Senthil Mahesh 1, Ch.Rupa Kalpana2, M. Rudra Kumar3
1P.C.Senthil Mahesh, Professor, Department of CSE, Annamacharya Institute of Technology & Sciences, Rajampet, Andhrapradesh, India-516126.
2Ch.Rupa Kalpana, PG Student, Department of CSE, Annamacharya Institute of Technology & Sciences, Rajampet, Andhrapradesh, India-516126.
3M.Rudra Kumar, Professor & HOD, Department of CSE, Annamacharya Institute of Technology & Sciences, Rajampet, Andhrapradesh, India-516126
Manuscript received on 26 August 2019. | Revised Manuscript received on 11 September 2019. | Manuscript published on 30 September 2019. | PP: 693-696 | Volume-8 Issue-11, September 2019. | Retrieval Number: K17340981119/2019©BEIESP | DOI: 10.35940/ijitee.K1734.0981119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Stress is a kind of demand to respond to any in your body’s manner. It can be based on experiences that are both good and bad. Psychological stress threatens the health of individuals. People are used to exchanging their schedule and daily operations with colleagues on social media platforms with the reputation of a social media network, creating it possible to hold online social network information for stress detection. For a variety of applications data mining methods are used. Data mining plays a significant role in the detection of stress in sector. We proposed a new model in this article to detect stress. Initially, in this model, discover a correlation between stress states of user and effective public interactions. This describes a set of textual, visual and social characteristics related to stress from different elements and proposes a new hybrid model coupled with Convolutional Neural Network (CNN) to efficiently hold tweet content and data on social interaction to detect stress. The suggested model can enhance the detection efficiency by 97.8 percent, which is quicker than the current scheme, from the experimental outcomes.
Keywords: Stress, Social Networking, Attribute Extraction, Factor Graph Construction and Stress Discovery
Scope of the Article: