Browsing by Author "Bandara, P.S"

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Embargo
Depression Detection System Using Real-Time and Social Media Data
(IEEE, 2022-12-09) Jayasinghe, G.C.J.; Shamika, I.P.M.A.; Dissanayake, G.A.I.P; Ranaweera, R.M.I.A; Bandara, P.S
The main objective of this study is to measure the depression level of the participants. The guidance will be provided by the psychiatrist to understand the parameters. The end system has been implemented to measure it with a live session with pre-designed questionnaire set. During the session time, the behavior of the participant has been captured through audio and video method. The long-term depression level measurement will be analyzing the social media behavior of the participant within a month. The Convolution Neural Network (CNN) and Natural Language Processing (NLP) are using to analyze the video, audio and text data. To analyze the results; The Beck Depression Inventory (BDI II) scale will be utilized. The accuracy of the output results measured as high as it has been individually analyzed the subcomponents and then predict to a one result.
Embargo
Measuring Psychological Stress Rate Using Social Media Posts Engagement
(IEEE, 2022-08-15) Perera, W.T. H; Lanerolle, T. Y; Andrado, Y. D. S; Wickramasinghe, W.A.P.C; Bandara, P.S; Kishara, J
In psychology, stress is a feeling of feelings and pressure. Stress is a type of psychological pain. Literature has showcased that mental health stages like anxiety and depression might be identified by the social media post captions, emojis, and the way users communicate with others. Among the main underlying causes and correlates of illnesses and mental health problems is stress. In this study, we explore the conclusions and posts of psychological stress using the data of social media users, who use and share their Facebook accounts. In the first step, a user who are stressed often post about exhaustion, losing control, increasing self-focus, and physical pain using their post captions, emojis, and post images they usually post on Facebook. Collect and read all the posts that are fetched via the social networks and then measure the stress level against different factors. Then the system demonstrates how the user interacts with the intelligent custom virtual AI counselor application thus innovated can be trained and be scaled to measure against the factors. Data can be collected by using Graph API, followed by machine learning techniques and natural language processing (NLP) techniques, and an intelligent custom AI virtual application to measure stress levels by different factors. Also, use AI techniques to build health guidance plans for everyone with the help of the above collections. And reacting to the simple games is another factor to measure a highly accurate result in stress level. Natural Language Processing (NLP) is commonly used to implement smart communication virtual counselor agents. Scaled social media-based stress measurements outperform survey-based stress measurements, held up against involving a combination of social and demographic factors such as gender, age, race, income, and education. A discussion of the implications of using social media as a new tool for monitoring stress levels and developing health-related advice for individuals is presented in the conclusion.