Machine Learning Prospects: Insights for Social Media Data Mining and Analytics

Anu Sharma; Vivek Kumar

doi:10.55524/ijircst.2023.11.3.3

Abstract

Social network has increased surprising consideration in the most recent decade .Social network deals with enormous volume of composite as well as unstructured data and they are very hard to handle. Due to expanding dimensions and demand, one of the encouraging and interesting research field becomes social network. Data Mining affirms to get knowledge by discovery patterns among data. We have discussed social media mining and Social Media analytics. We have insights on the social media effect of our lives, some facts and reports from various sources. We have Integrated this growing research field of social networks with Machine Learning with one simple example of sentiment analysis of Twitter data using Machine Learning. We have also proposed the algorithms to improve the social media analytics results using Machine Learning. In this paper, we will exhibit how machine learning will utilizing for social networking systems like Twitter. In this procedure, a framework is proposed that will collect the tweets messages from the and we will inspect the item’s input to show the positive, negative, or nonpartisan tweets, for this this purpose we have proposed new machine learning algorithms Naive Bayes, maximum entropy to find these outputs. Our proposed Model will help new researchers, companies, Industries, business community, practitioners, new integrated application designers, and the global community to solve the new research problem and may reducing design failure rate of 80% by large through social media mining and networks.

Keywords

Data mining, Social media data, deep learning, Machine learning, Naïve Bayes, Maximum entropy

References

Relationships and Technology Lab, College of Liberal Arts & Sciences: https://randtlab.ku.edu/jeffrey-hall. [accessed on: January 18, 2021]
Twenge JM, Spitzberg BH, Campbell WK. Less in-person social interaction with peers among U.S. adolescents in the 21st century and links to loneliness. Journal of Social and Personal Relationships. 2019;36(6):1892-1913. doi:10.1177/0265407519836170.
Social media’s growing impact on our lives https://www.apa.org/members/content/social-media-research- series. [accessed on: January 18, 2021]
Y. Zhao et al., "IEEE Access Special Section Editorial: Advanced Data Mining Methods for Social Computing," in IEEE Access, vol. 8, pp. 228598-228604, 2020, doi: 10.1109/ACCESS.2020.3043060.
Tajeuna Etienne Gael, Bouguessa Mohamed, and Wang Shengrui. Modeling and predicting community structure changes in time evolving social networks. IEEE Transactions on Knowledge Data Engineering, pages 1–1.
Zhaoyi Li , Fei Xiong , Ximeng Wang, Hongshu Chen and Xi Xiong, Topological Influence-Aware Recommendation on Social Networks, Complexity, 2019, Article no. 6325654.
Barabasi, A.L. (2002). Linked: The New Science of Networks. Cambridge, MA: Perseus
Maddon, M. & Fox, S. (2006). Riding the waves of "Web2.0": More than a buzzword, but still not easy to define. Pew Internet Project. Retrieved from http://www.pewinternet.org/pdfs/PIP_Web_2.0.pdf
E.K Clemons., “ The Future of Advertising and the Value of Social Network Websites: Some Preliminary Examinations”. Minneapolis, Minneosta, USA, 2007.
D. Boyd., “Social Network Sites: Definition, History, and Scholarship”,Journal of Computer-Mediated Communication 13 (1), 2007.
“Social Network Marketing: The Basics” Available: http://www.labroots.com/Social_Networking_the_Basics.pdf [Aug 01,2013].
Nandi,G., Das, A.:Asurvey on using datamining techniques for online social network analysis. Int. J. Comput. Sci. Issues (IJCSI) 10(6), 162 (2013)
[13]. Batrinca, B., Treleaven, P.C.: Social media analytics: a survey of techniques, tools and platforms. Ai Soc.30(1), 89–116 (2015).
J.W. Seifert. “Data Mining: An Overview”, CRS Report for Congress, 2004 [Online] Available: http://www.fas.org/irp/crs/RL31798.pdf [Sep 05 2013]
H. Kob, G, Tan, “Data Mining Applications in Healthcare” ,Nov 10 2010.
M Hart. “Progress of organisational data mining in South Africa”, Department of Information Systems, University of Cape Town, South Africa, 2006. [Online] Available: http://intranet.inria.fr/international/arima/006/pdf/arima00601.pdf [Jun 5, 2011]
Neelamadhab Padhy1, Dr. Pragnyaban Mishra 2, and Rasmita Panigrahi3, “The Survey of Data Mining Applications And feature scope”, in International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, Issue.3, 2012.
Smita1, Priti Sharma, “ Use of Data Mining in Various Field: A Survey Paper”, in IOSR Journal of Computer Engineering (IOSR-JCE), Volume 16, Issue 3, PP 18-21, 2014.
Mrs. Bharati M. Ramageri,” DATA MINING TECHNIQUES AND APPLICATIONS”, in Indian Journal of Computer Science and Engineering, Vol. 1 Issue. 4, PP: 301-305.
Annan Naidu Paidi “ Data Mining: Future Trends and Applications” in International Journal of Modern Engineering Research (IJMER), Vol.2, Issue.6, PP:4657-4663, 2012.
Umamaheswari. K, S. Niraimathi “A Study on Student Data Analysis Using Data Mining Techniques”, in International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 8, PP:117-120, 2013.
E.Veerman, T.Lachev, D.Sarka, Microsoft SQL Server 2008- Bussiness Intelligence Development and Manitenance, Microsof, PHI Learning Private Limited, pp 372 – 380, 2009.
Nikita Jain, Vishal Srivastava “DATA MINING TECHNIQUES: A SURVEY PAPER” in IJRET: International Journal of Research in Engineering and Technology, Volume: 02, Issue: 11, PP:116-119, 2013. [24]. Ranshul Chaudhary1, Prabhdeep Singh2, Rajiv Mahajan3, “A SURVEY ON DATA MINING TECHNIQUES” in International Journal of Advanced Research in Computer and Communication
Engineering, Vol. 3, Issue 1, PP:5002-5003, 2014.
Social Network Usage & Growth Statistics: How Many People Use Social Media in 2020? [Online] Available: https://backlinko.com/social-media-users [Jan 23, 2021]
GLOBAL SOCIAL MEDIA OVERVIEW [OCT 2020] [Online] Available: https://datareportal.com/social- media-users [Jan 23, 2021]
Global social media research summary August 2020 [Online] Available: https://www.smartinsights.com/social-media-marketing/social-media-strategy/new-global-social-media- research/ [Jan 23, 2021]
Adedoyin-Olowe, M., Gaber, M. M., & Stahl, F, “A Survey of Data Mining Techniques for Social Media Analysis” in Journal of Data Mining & Digital Humanities, PP:1-27, 2014.
S.G.S Fernando et.al “Empirical Analysis of Data Mining Techniques for Social Network Websites” in COMPUSOFT, An international journal of advanced computer technology, Volume-III, Issue-II PP:582- 592, 2014.
Thabit Zatari, “Data Mining in Social Media” in International Journal of Scientific & Engineering Research, ISSN 2229-5518 Volume 6, Issue 7, PP:152-154, 2015.
Dr.B.Umadevi, P.Surya, “A Review on Various Data Mining Techniques in Social Media”, in International Journal of Innovative Research in Computer and Communication Engineering, Vol. 5, Issue 4, PP: 8082-8086, 2017.
Mohammad Noor Injadat, Fadi Salo, Ali Bou Nassif “Data Mining Techniques in Social Media: A Survey”, NEUCOM17295, Volume 214, PP:654-670, 2016.
Rahman, M. M, “Mining Social Data to Extract Intellectual Knowledge”, in International Journal of Intelligent Systems and Applications(IJISA), vol.4, no.10, PP:15-24, 2012.
XimingWang · Panos M. Pardalos, “A Survey of Support Vector Machines with Uncertainties”, © Springer-Verlag Berlin Heidelberg 2015, Ann. Data. Sci. (2014) 1(3–4) PP:293–309, 2014
D. M. Boyd and N. B. Ellison, “Social network sites: Definition, history, and scholarship” Journal of computer-mediated communication, vol. 13, no. 1, pp. 210-230, 2007.
J. F. Sánchez-Rada and C. A. Iglesias, “Social context in sentiment analysis: Formal definition, an overview of current trends and framework for comparison” Information Fusion, vol. 52, pp. 344-356, 2019.
C. Haythornthwaite, “Social network analysis: An approach and technique for the study of information exchange” Library & information science research, vol. 18, no. 4, pp. 323-342, 1996.
Z. Yang, R. Zheng, and Y. Ma, “Parallel Heuristics for Balanced Graph Partitioning Based on Richness of Implicit Knowledge” in IEEE Access, vol. 7, pp. 96444-96454, 2019, doi 10.1109/ACCESS.2019.2926753. [39]. Z. Zhuang, C. Wei, B. Li, P. Xu, Y. Guo, and J. Ren, “Performance Prediction Model Based on Multi-Task Learning and Co-Evolutionary Strategy for Ground Source Heat Pump System” in IEEE Access, vol. 7, pp. 117925-117933, 2019, doi: 10.1109/ACCESS.2019.2936508.
X. Gong and S. Zhu, “Person Re-Identification Based on Two-Stream Network With Attention and Pose Features” in IEEE Access, vol. 7, pp. 131374-131382, 2019, doi: 10.1109/ACCESS.2019.2935116.
Anu Sharma et.al, “Literature Review and Challenges of Data Mining Techniques for Social Network Analysis”, Advances in Computational Sciences and Technology ISSN 0973-6107 Volume 10, Number 5 (2017) pp. 1337-1354
Anu Sharma et.al, “Hybrid Neuro-Fuzzy Classification Algorithm for Social Network”, International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-8 Issue-6, August 2019 https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8673951&tag=1

Cites this article as

[Anu Sharma, Vivek Kumar (2023), Machine Learning Prospects: Insights for Social Media Data Mining and Analytics, International Journal of Innovative Research in Computer Science and Technology (IJIRCST), Vol-11, Issue-3, Page No-12-19], (ISSN 2347 - 5552). www.ijircst.org

Corresponding Author

Anu Sharma

Assistant Professor, Department of Computer Science & Engineering, Teerthanker Mahaveer University, Moradabad, India

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