Detection of Stress Level in Social Media users using Cnn-Fg Model
Tanzila Nargis1, Nikitha Saurabh2
1Tanzila NargisCurrently working as an Assistant Professor in the Department of Information Science and Engineering at NMAMIT, Nitte, Karkala, Karnataka.
2Nikitha Saurabh Currently working as an Assistant Professor in the Department of Information Science and Engineering at NMAMIT, Nitte, Karkala.
Manuscript received on November 19, 2019. | Revised Manuscript received on November 29 2019. | Manuscript published on 30 November, 2019. | PP: 9733-9736 | Volume-8 Issue-4, November 2019. | Retrieval Number: D9248118419/2019©BEIESP | DOI: 10.35940/ijrte.D9248.118419
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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: Psychological stress has become a common condition in today’s world owing to the busy life style and competitive environment. This has led to increase of suicidal rates in the recent years. Lately, there has been a tremendous increase in interactions in the social networking sites. As people are spending long hours in the virtual world it is easier to detect and analyze the stress levels of the social media users. In this paper, we have proposed a hybrid approach which is a combination of Factor Graph (FG) model and Convolutional Neural Network (CNN) to analyze the textual contents in social media users’ tweets and posts to detect the level of stress of a user. The tweets of an individual user are gathered from Twitter platform which is preprocessed and passed to the cross autoencoder embedded CNN Model which outputs user level attributes. These are then input to the Factor Graph model that detects the stressed tweets. A mechanism has been proposed to inform the friends or relatives of the concerned stressed user if the detected stress level is above the given threshold.
Keywords: Stress Detection, Factor Graph model, CNN, Social Media.
Scope of the Article: Graph Algorithms and Graph Drawing.