Document Type : Original Article
Authors
Abstract
BACKGROUND: Social media platforms such as Facebook, WhatsApp, and Instagram etc., are
becoming very popular now not only for youth but for all walks of life. People are more often seen in
busy in tweeting, chatting, or putting selfies. No one actually knows the mental state of a person in
the online platform. In this article, we will be focusing on how social media is affecting issues such
as road accident, murder, and suicide. The research is done by three parts.
MATERIALS AND METHODS: Google Form analysis, machine learning used for prediction, and by
sentimental analysis of what people think in twitter. All the datasets are based in India. From these
datasets, the different machine learning algorithm is used to do the analysis. The project strives to
bring the real‑world solution in the matter of advancement.
RESULTS: The static data analysis and dynamic data analysis shows the various sentimental analysis
and predictions and the technique to predict different mental states. Thus we get clearly about the
current world is getting into social issues. This research findings helps to bring social awareness
among the current generation by understanding the sensitivity of the youths.
CONCLUSION: Thus through this paper we get known clearly how the current world is getting
into social issues like victim of murders or road accidents or committing suicide. The paper clearly
helps us to understand the sensitivity of the youths. Therefore brings a social awareness among
the current generation.
Keywords
Using Machine Learning Paradigms. Informatica (Slovenia), 2005.
p. 89‑98.
2. Vasavi S. Extracting hidden patterns within road accident
data using machine learning techniques. In: Information
and Communication Technology. Advances in Intelligent
Systems and Computing, Springer, Singapore, Springer; 2018.
p. 13‑22.
3. Clarke DD, Forsyth R, and Wrights R. Machine learning in road
accident research: Desicion trees describing road accidents during
croos‑flow turns. Processes and countermeasures in overtaking
road accidents. Ergonomics, 1999, 42:6, p. 846‑867.
4. Nofal EM. Utilization of Social Media for Raising Required
Awareness of Human Factors Affecting Traffic Safety; National
Partnership and Corporate Social Responsibility”; 2013,
Proceedings of The Second Forum “Traffic Safety: National
Partnership and Corporate Social Responsibility”Dammam,
Kingdom of Saudi Arabia, p. 204 – 215.
5. Calderon‑Vilca HD, Wun‑Rafael WL, Loarte MR., Machine
Learning and Metaheuristics Algorithms, and Applications, First
Symposium, SoMMA 2019, Trivandrum, India, December 18–21,
2019.
6. Birjalia M, Hssanea AB, Erritalib M. Machine Learning and
Semantic Sentiment Analysis based Algorithms for Suicide
Sentiment Prediction in Social Networks 2017. Procedia Computer
Science, 113, 2, 2017, p. 65‑72.
7. Glen C, Leary R, Crutchley P, Fine A. Natural Language
Processing of Social Media as Screening for Suicide Risk;
Biomedical Informatics Insights, Proceedings from the Digital
Mental Health Conference‑London 2018.
8. Colomboa GB, Burnap P, Hodorog A, Scourfield J. Analyzing
the connectivity and communication of suicidal users on twitter.
Computer Communications, 73,1,2015, p. 291‑300.
9. McClendon L, Meghanathan N. Using machine learning
algorithms to analyze crime data. Machine Learning and
Applications: An International Journal (MLAIJ) 2,1,2015,
p. 11‑21.
10. Prabakaran S, Mitra S. Survey of analysis of crime detection
techniques using data mining and machine learning. Journal of
Physics: Conference Series, 2018, DOI :10.1088/1742‑6596/1000
/1/012046.
11. Paul Elhorst J. Research on the Raw Data Processing Method of
the Hydropower Construction Paper; IOP Conference Series:
Earth and Environmental Science, 108,5, 2017, p1‑5.
12. Vijayarani S, Sharmila S. Research in big data – An overview.
Journal of Xi’an University of Architecture & Technology, 2016,
p. 1604‑1610.
)