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Muhammed Yaseen Morshed Adib

Work place: Southeast University, Dhaka, 1208, Bangladesh

E-mail: yaseen.morshed@seu.edu.bd

Website: https://orcid.org/0009-0003-0012-8901

Research Interests:

Biography

Muhammed Yaseen Morshed Adib, was born in Chittagong, Bangladesh, on December 25, 1995. He earned a Bachelor of Science degree in computer science and engineering from Ahsanullah University of Science and Technology, Dhaka, Bangladesh, in 2018, and a Master of Science degree in computer science and engineering from BRAC University, Dhaka, Bangladesh, in 2024. His major field of study is computer science, with a focus on natural language processing, machine learning, deep learning, and sentiment analysis.
He is currently a Lecturer in the Department of Computer Science and Engineering at Southeast University, located at 252, Tejgaon Industrial Area, Dhaka - 1208, Bangladesh. He has previously worked as a full-time faculty member at Stamford University Bangladesh. His recent publications include BiLSTM-ANN Based Em- ployee Job Satisfaction Analysis from Glassdoor Data Using Web Scraping (Elsevier, 2023), LSTM-ANN Based Price Hike Sentiment Analysis from Bangla Social Media Comments (IEEE, 2023), and CNN-GRU Based Fu- sion Architecture For Bengali License Plate Recognition With Explainable AI (IEEE, 2024).

Author Articles
Impact of 2023 Turkey Earthquake Price Hikes: Insightful Socio-Economic Analysis Using Transformer Models and Explainable AI

By Muhammed Yaseen Morshed Adib Md. Tauhid Bin Iqbal Farig Yousuf Sadeque

DOI: https://doi.org/10.5815/ijieeb.2025.05.05, Pub. Date: 8 Oct. 2025

Natural disasters cause economic instability, leading to severe financial hardships for affected communities. The rapid surge in essential goods prices during such events significantly burdens vulnerable populations, highlighting the critical need for timely policy interventions. While understanding public sentiment on economic distress is crucial for effective data-driven policy generation, research specifically analyzing public sentiment on price hikes in such contexts remains limited, often due to a lack of dedicated datasets. To address this, this paper first introduces a novel dataset of social media comments on price hikes related to the 2023 Turkey earthquake. Second, to support data-driven policy-making by quantifying public sentiment, we applied a range of AI models and identified transformer-based models like DistilBERT as particularly effective for sentiment classification. Furthermore, we employ Explainable AI techniques to enhance model trust, enabling policymakers to confidently use these insights to support disaster recovery and economic stabilization in affected regions.

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