A novel attention-based deep learning model for improving sentiment classification after the case of the 2023 Kahramanmaras/Turkey earthquake on Twitter

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dc.contributor.authorAslan, Serpil
dc.contributor.authorYildirim, Muhammed
dc.date.accessioned2026-06-19T06:37:29Z
dc.date.available2026-06-19T06:37:29Z
dc.date.issued2025
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractTwitter has emerged as one of the most widely used platforms for sharing information and updates. As users freely express their thoughts and emotions, a vast amount of data is generated, particularly in the aftermath of disasters, which can be collected quickly and directly from individuals. Traditionally, earthquake impact assessments have been conducted through field studies by non-governmental organizations (NGOs), a process that is often time-consuming and costly. Sentiment analysis (SA) on Twitter presents a valuable research area, enabling the extraction and interpretation of real-time public perceptions. In recent years, attention-based methods in deep learning networks have gained significant attention among researchers. This study proposes a novel sentiment classification model, MConv-BiLSTM-GAM, which leverages an attention mechanism to analyze public sentiment following the 7.8 and 7.5 Mw earthquakes that struck Kahramanmara & scedil;, Turkey. The model employs the FastText word embedding technique to convert tweets into vector representations. These vectorized inputs are then processed by a hybrid model integrating convolutional neural networks (CNNs) and recurrent neural networks (RNNs) with a global attention mechanism. This ensures careful consideration of semantic dependencies in sentiment classification. The proposed model operates in three stages: (i) MConv-Local Contextual Feature Extraction, (ii) bidirectional long short-term memory (BiLSTM)-sequence learning, and (iii) Global Attention Mechanism (GAM)-Attention Mechanism. Experimental results demonstrate that the model achieves an accuracy of 93.32%, surpassing traditional deep learning models in the literature by approximately 3%. This research aims to provide objective insights to policymakers and decision-makers, facilitating adequate support for individuals and communities affected by disasters. Moreover, analyzing public sentiment during earthquakes contributes to understanding societal responses and emotional trends in disaster scenarios.
dc.identifier.doi10.7717/peerj-cs.2881
dc.identifier.issn2376-5992
dc.identifier.orcid0000-0003-1866-4721
dc.identifier.orcid0000-0001-8009-063X
dc.identifier.pmid40567692
dc.identifier.scopus2-s2.0-105005181146
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.7717/peerj-cs.2881
dc.identifier.urihttps://hdl.handle.net/20.500.12899/5038
dc.identifier.volume11
dc.identifier.wosWOS:001488652100001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherPeerj Inc
dc.relation.ispartofPeerj Computer Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260612
dc.subjectEarthquake
dc.subjectDeep Learning
dc.subjectSentiment Classification
dc.subjectAttention Mechanism
dc.subjectCnn
dc.subjectRnn
dc.subjectFasttext
dc.titleA novel attention-based deep learning model for improving sentiment classification after the case of the 2023 Kahramanmaras/Turkey earthquake on Twitter
dc.typeArticle

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