Mining Social Media Data: A Practical Approach with Weka

Pardeep Arora; Jass Kaur; Anshu

doi:10.32628/IJSRST25123108

Authors

Pardeep Arora Department of IT, Kanya Maha Vidyalaya, Jalandhar, Punjab, India Author
Jass Kaur Department of IT, Kanya Maha Vidyalaya, Jalandhar, Punjab, India Author
Anshu Department of IT, Kanya Maha Vidyalaya, Jalandhar, Punjab, India Author

DOI:

https://doi.org/10.32628/IJSRST25123108

Keywords:

Data Mining, Social Media, Analysis, Data sets, Machine learning algorithms

Abstract

Due to the massive amount of data produced by social media's explosive growth, analysis and insight extraction are becoming more difficult. This study focuses on machine learning classification problems on various social media datasets. The datasets include "Time-Waster on Social Media," "Instagram Profile," "Instagram Photos," along with "Viral Trends." The Bayes Network, Random Forest, Decision Tree (J48), and Naive Bayes algorithms were employed. On the "Time-Waster" and "Instagram Photos" datasets, Random Forest outperformed the others. Machine learning algorithms like F-Measure, Precision, Recall, and ROC AUC were employed in the evaluation. Multimedia content may be investigated in future research to gain a greater understanding of user trends and behavior.

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