Risk Monitoring and Quantitative Results of Various Attributes of Machine Learning Algorithms with a Time Series Data
Rakhi Gupta1, Nashrah2, SD Joshi3, Suhas patil4
1Rakhi Gupta, PHd, Department of Computer Enggineering from Bharati Vidyapeeth Deemed University Pune, Maharashtra, India.
2Nashrah, Assistant Professor, Department of Information Technology from K.C. College Mumbai, Maharashtra, India.
3Dr. Shashank Joshi, Professor in Computer Engineering Department Bharati Vidyapeeth Deemed University College of Engineering, Pune, Maharashtra, India.
4Dr. Suhas Patil, Professor in Computer Engineering Department Bharati Vidyapeeth Deemed University College of Engineering, Pune, Maharashtra, India.
Manuscript received on 23 August 2019. | Revised Manuscript received on 05 September 2019. | Manuscript published on 30 September 2019. | PP: 4018-4022 | Volume-8 Issue-11, September 2019. | Retrieval Number: J95700881019/2019©BEIESP | DOI: 10.35940/ijitee.J9570.0981119
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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: The aim of this research is to do risk modelling after analysis of twitter posts based on certain sentiment analysis. In this research we analyze posts of several users or a particular user to check whether they can be cause of concern to the society or not. Every sentiment like happy, sad, anger and other emotions are going to provide scaling of severity in the conclusion of final table on which machine learning algorithm is applied. The data which is put under the machine learning algorithms are been monitored over a period of time and it is related to a particular topic in an area.
Keywords: Risk modelling, Machine learning algorithms, Emotions, Sentiment analysis
Scope of the Article: Machine Learning.