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General Article

A sentiment-aware hybrid deep learning framework for sustainable smart urban and social network ecosystems
Shaifun Nahar Sonia1
,
Rachita Kansal2
,
Chander Diwaker3*
1Lecturer, Department of Mathematics, University of Science and Technology Chittagong, Bangladesh
2Associate Professor, Department of Computer Science and Engineering, UIET, Kurukshetra University, Kurukshetra, India
3Research Scholar, Department of Computer Science and Engineering, UIET, Kurukshetra University, Kurukshetra, India
*Corresponding Author
31 December 2025. pp. 494-509
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Abstract

This paper presents an emotion-sensitive hybrid deep-learning platform to facilitate smart city and social-networking implementations in order to achieve sustainability. The system is a combination of Sentiment analysis (SA), Contend based filtering (CBF), and Collaboration filtering (CF) that provides context-based recommendations that increase user engagement and promotes sustainable digital interactions. BERT and RoBerta are used to classify emotions and combine the sentiment understanding with behavioral and content features to produce highly accurate and specific recommendations. This framework was tested using two real datasets, and the accuracy was 99.14%, which is better than other state of art methods. These findings indicate the role of sentiment sensitive Deep-Learning (DL) platforms in generating human-focused, intelligent and sustainable social- network ecosystems to enhance the objectives of smart urbanism.

Keywords
sentiment analysis
recommendation
deep learning
social networks
smart urban sustainability
collaboration filtering
BERT
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Information
  • Publisher :Sustainable Building Research Center (ERC) Innovative Durable Building and Infrastructure Research Center
  • Publisher(Ko) :건설구조물 내구성혁신 연구센터
  • Journal Title :International Journal of Sustainable Building Technology and Urban Development
  • Volume : 16
  • No :4
  • Pages :494-509
  • Received Date : 2025-10-29
  • Accepted Date : 2025-11-18
  • DOI :https://doi.org/10.22712/susb.20250033
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