Regional trending topics mining from real time Twitter data for sentiment, context, network and temporal analysis

Mousumi Hasan; Mujiba Shaima; Quazi Saad ul Mosaher

doi:10.35784/iapgos.6478

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Published: Mar 31, 2025

DOI: https://doi.org/10.35784/iapgos.6478

DOI

https://doi.org/10.35784/iapgos.6478

Authors

Mousumi Hasan

mousumi.cse@baiust.ac.bd

Bangladesh Army International University of Science and Technology, Department of Computer Science and Engineering

https://orcid.org/0009-0006-7499-7669

Mujiba Shaima

goodmorn_simi@yahoo.com

Monroe University, Department of Computer Science and Engineering

https://orcid.org/0009-0004-9129-0716

Quazi Saad ul Mosaher

saad.quazi44@gmail.com

Bangladesh Army International University of Science and Technology, Department of Computer Science and Engineering

Abstract

Twitter's tremendous impact extends from national to international affairs, covering domains such as religion, entertainment, environment, and politics. Unlike other social media platforms, Twitter offers researchers an open data source for multifaceted studies, motivating us to delve into regional trending topics, analyzing associated sentiments, context, networks, and temporal patterns. Using machine learning techniques combined with the VADER algorithm, we conducted a comprehensive analysis involving text, metadata, contextual cues, media, links, and historical data. In this study, we conducted an extensive Twitter mining operation on June 6, 2024, focusing on the ten most developed countries to explore sentiments associated with sustainable technology in industry. The insights derived from this research are pivotal for policymakers, industry stakeholders, and researchers, offering a nuanced understanding of public opinion on sustainable technology. Our findings underscore the potential of social media mining as a powerful tool for gauging public sentiment and informing strategic decision-making in the realm of sustainable industrial practices.

Keywords:

twitter mining, sentiment analysis, network analysis, temporal analysis

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Hasan, M., Shaima, M., & Mosaher, Q. S. ul. (2025). Regional trending topics mining from real time Twitter data for sentiment, context, network and temporal analysis. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 15(1), 109–116. https://doi.org/10.35784/iapgos.6478

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