A deep learning-based sentiment analysis approach (MF-CNN-BILSTM) and topic modeling of tweets related to the Ukraine-Russia conflict

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dc.contributor.authorAslan, Serpil
dc.date.accessioned2026-06-19T06:40:52Z
dc.date.available2026-06-19T06:40:52Z
dc.date.issued2023
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractTwitter, one of the most significant social media platforms, can be used as data sources to research public opinion on various topics, including political conflicts. People worldwide have expressed their opinions about the war between Russia and Ukraine since it began. The motivation of this study is to use deep learning techniques to reveal qualitative and quantitatively narrow-scoped situational awareness and emotional tendencies during crisis periods. In order to achieve this, a sizable dataset of geotagged tweets was gathered with specific terms associated with the conflict between Ukraine and Russia. Then, deep learning-based text mining techniques like topic modeling and sentiment analysis were applied to investigate people's perspectives on conflict and emotional tendencies. The study will establish an impartial data source for the reports and articles of unbiased press members. This study used Valence Aware Dictionary and sEntiment Reasoner (VADER) to categorize the emotions related to the conflict between Russia and Ukraine in tweets. In addition, Latent Dirichlet Allocation (LDA) was used to extract various discussion topics. These techniques reveal the role of Twitter and compare and analyze the emotions and attitudes expressed on Twitter by different countries during the Ukraine-Russia conflict. In addition, a new deep learning-based sentiment classification MF-CNN-BiLSTMmodel that predicts and analyzes sentiments was proposed in this study. The proposed model stands for Multistage Feature Extraction using Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM). By combining various qualities and benefits of CNNs and BiLSTM, the suggested model makes it possible to identify both short-and long-term dependencies in ordinal data. The proposed model includes MF steps that strengthen feature extraction by identifying local dependencies. Experimental results demonstrate that it is possible to obtain more accurate sentiment classification results by the proposed method.& COPY; 2023 Elsevier B.V. All rights reserved.
dc.identifier.doi10.1016/j.asoc.2023.110404
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.orcid0000-0001-8009-063X
dc.identifier.scopus2-s2.0-85162765561
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.asoc.2023.110404
dc.identifier.urihttps://hdl.handle.net/20.500.12899/5949
dc.identifier.volume143
dc.identifier.wosWOS:001021086100001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorAslan, Serpil
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofApplied Soft Computing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20260612
dc.subjectUkraine-Russia Conflict
dc.subjectDeep Learning
dc.subjectTwitter Sentiment Analysis
dc.subjectCnn
dc.subjectBilstm
dc.subjectFasttext
dc.subjectTopic Modeling
dc.titleA deep learning-based sentiment analysis approach (MF-CNN-BILSTM) and topic modeling of tweets related to the Ukraine-Russia conflict
dc.typeArticle

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