MBi-GRUMCONV: A novel Multi Bi-GRU and Multi CNN-Based deep learning model for social media sentiment analysis

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dc.authoridBAŞARSLAN, MUHAMMET SİNAN/0000-0002-7996-9169
dc.authoridKayaalp, Fatih/0000-0002-8752-3335
dc.authorwosidKayaalp, Fatih/HPG-9242-2023
dc.authorwosidBAŞARSLAN, MUHAMMET SİNAN/AAH-2116-2020
dc.contributor.authorBaşarslan, Muhammet Sinan
dc.contributor.authorKayaalp, Fatih
dc.date.accessioned2023-07-26T11:58:26Z
dc.date.available2023-07-26T11:58:26Z
dc.date.issued2023
dc.departmentDÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractToday, internet and social media is used by many people, both for communication and for expressing opinions about various topics in many domains of life. Various artificial intelligence technologies-based approaches on analysis of these opinions have emerged natural language processing in the name of different tasks. One of these tasks is Sentiment analysis, which is a popular method aiming the task of analyzing people's opinions which provides a powerful tool in making decisions for people, companies, governments, and researchers. It is desired to investigate the effect of using multi-layered and different neural networks together on the performance of the model to be developed in the sentiment analysis task. In this study, a new, deep learning-based model was proposed for sentiment analysis on IMDB movie reviews dataset. This model performs sentiment classification on vectorized reviews using two methods of Word2Vec, namely, the Skip Gram and Continuous Bag of Words, in three different vector sizes (100, 200, 300), with the help of 6 Bidirectional Gated Recurrent Units and 2 Convolution layers (MBi-GRUMCONV). In the experiments conducted with the proposed model, the dataset was split into 80%-20% and 70%-30% training-test sets, and 10% of the training splits were used for validation purposes. Accuracy and F1 score criteria were used to evaluate the classification performance. The 95.34% accuracy of the proposed model has outperformed the studies in the literature. As a result of the experiments, it was found that Skip Gram has a better contribution to classification success.en_US
dc.identifier.doi10.1186/s13677-022-00386-3
dc.identifier.issn2192-113X
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85146117025en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1186/s13677-022-00386-3
dc.identifier.urihttps://hdl.handle.net/20.500.12684/13490
dc.identifier.volume12en_US
dc.identifier.wosWOS:000912011400001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorKayaalp, Fatih
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofJournal of Cloud Computing-Advances Systems and Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz$2023V1Guncelleme$en_US
dc.subjectNatural Language Processing; Sentiment Analysis; Word2vec; Word Embedding; Deep Learningen_US
dc.subjectAspect Extractionen_US
dc.titleMBi-GRUMCONV: A novel Multi Bi-GRU and Multi CNN-Based deep learning model for social media sentiment analysisen_US
dc.typeArticleen_US

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