Mood Mechanic
Srishti C Rai1, Sheetal Vernekar2, Ajay L Gowda3, Nishith A4, Prathima Anand5
1Srishti C Rai, Graduate, Department of Computer Science and Engineering, RNS Institute of Technology, Bangalore (Karnataka), India.
2Sheetal Vernekar, Graduate, Department of Computer Science and Engineering, RNS Institute of Technology, Bangalore (Karnataka), India.
3Ajay L Gowda, Department of Information Science and Engineering, RNS Institute of Technology, Bangalore (Karnataka), India.
4Nishith A, Department of Information Science and Engineering, RNS Institute of Technology, Bangalore (Karnataka), India.
5Prathima Anand, Department of Electronics and Instrumentation Engineering, RNS Institute of Technology, Bangalore (Karnataka), India.
Manuscript received on 05 December 2019 | Revised Manuscript received on 13 December 2019 | Manuscript Published on 31 December 2019 | PP: 414-417 | Volume-9 Issue-2S December 2019 | Retrieval Number: B10401292S19/2019©BEIESP | DOI: 10.35940/ijitee.B1040.1292S19
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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: Depression is a major problem being faced by a lot of people. It is the extremely low mood faced by an individual. Some cope up with this mood change very quickly but some drastically fall into it. Those who fall into it suffer from depression. Prediction of a person’s mood plays a major role in treatment of depression. But predicting a person’s mood from previously collected data is challenging. Mood of a person can depend on various factors such body language, facial expressions and current mind state .But mood prediction is not enough, instead the proposed system involves ways in which we can use the predicted data to provide assistance in case of any deviation from a healthy mental condition. Past approaches being used, predict mood considering only a few parameters .This can lead to results being less accurate making it less reliable. A lot of these issues can be handled by the ‘Mood Mechanic’ approach. This paper mainly emphasizes on the existing approaches related to mood prediction and their limitations so as to propose a system that would not only help in efficient prediction but also help in assisting the user of the system on the further actions to be taken based on the predicted results. This approach considers many parameters such as facial expressions, social media usage and self-evaluation results. On collecting all these data and performing analysis on them, the system will suggest the actions or solutions, which will help the person in deciding on tasks which are generally suggested and are necessary for getting better.
Keywords: Depression, Mood Prediction, Mental Wellbeing, Sentimental analysis.
Scope of the Article: Regression and Prediction