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VOL. 11, ISSUE 1 (2026)
Detecting barriers and opportunities for women empowerment in Jammu & Kashmir through social media data mining and machine learning approaches
Authors
Uzma Hamid
Abstract
Women empowerment is a critical component of
inclusive socio-economic development, particularly in regions experiencing
structural inequalities and developmental transitions. Jammu and Kashmir
(J&K) represents a region where socio-economic reforms, digital expansion,
and institutional policy interventions are gradually transforming the role of
women in education, employment, and entrepreneurship. Despite these
improvements, several structural barriers such as employment limitations,
restricted access to higher education, safety concerns, and digital literacy
gaps continue to influence women’s empowerment outcomes. At the same time,
increasing smartphone penetration and social media usage have created new
platforms through which women share experiences, discuss challenges, and
explore empowerment opportunities. This research proposes a comprehensive
social media data mining and machine learning–based analytical framework
designed to detect empowerment-related barriers and opportunities in Jammu and
Kashmir. The study uses Natural Language Processing (NLP), sentiment analysis,
topic modeling, and predictive classification methods to analyze
empowerment-related discussions from large-scale social media datasets. Results
indicate that employment access, skill development opportunities, education
availability, and digital inclusion emerge as dominant themes influencing
empowerment outcomes. The findings demonstrate that machine learning–driven
analytics can serve as a powerful decision-support mechanism for policymakers,
enabling data-driven planning of gender-inclusive development programs.
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Pages:40-44
How to cite this article:
Uzma Hamid "Detecting barriers and opportunities for women empowerment in Jammu & Kashmir through social media data mining and machine learning approaches". International Journal of Advanced Science and Research, Vol 11, Issue 1, 2026, Pages 40-44
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