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VOL. 9, ISSUE 2 (2025)
Machine learning–based sentiment and topic modeling of social media narratives on women empowerment in Jammu & Kashmir
Authors
Uzma Hamid
Abstract
In the digital era, social media platforms
have evolved into powerful communication environments where individuals
actively exchange ideas, share experiences, and participate in discussions
related to social development. Among the many issues debated online, women
empowerment has emerged as a central theme, particularly in regions undergoing
socio-economic transformation. Understanding how empowerment-related narratives
are expressed in online spaces is essential for policymakers and development
organizations seeking to design inclusive and effective gender-focused
interventions. Regions such as Jammu & Kashmir present a unique
socio-cultural and developmental context in which social media discussions
often reflect both progress in empowerment initiatives and the persistence of
structural challenges. Therefore, systematic analysis of such digital
conversations can provide meaningful insights into public perceptions and
emerging policy priorities. The present research develops a machine
learning–based analytical framework to examine sentiment patterns and thematic
structures within social media narratives related to women empowerment in Jammu
& Kashmir. By transforming large volumes of unstructured social media data
into actionable insights, the proposed approach provides valuable evidence that
can assist policymakers, researchers, and social organizations in designing
targeted empowerment strategies. The findings highlight the growing role of
artificial intelligence–driven social media analytics as an essential tool for
supporting gender-inclusive governance and data-driven public policy planning
in diverse regional contexts such as Jammu & Kashmir.
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Pages:12-16
How to cite this article:
Uzma Hamid "Machine learning–based sentiment and topic modeling of social media narratives on women empowerment in Jammu & Kashmir". International Journal of Advanced Engineering and Technology, Vol 9, Issue 2, 2025, Pages 12-16
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