LSTM Network Based Sentiment Analysis for Customer Reviews

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dc.authoridHorasan, Fahrettin/0000-0003-4554-9083
dc.authoridBilen, Burhan/0000-0002-3106-7369
dc.contributor.authorBilen, Burhan
dc.contributor.authorHorasan, Fahrettin
dc.date.accessioned2025-01-21T16:33:50Z
dc.date.available2025-01-21T16:33:50Z
dc.date.issued2022
dc.departmentKırıkkale Üniversitesi
dc.description.abstractContinuously increasing data bring new problems and problems usually reveal new research areas. One of the new areas is Sentiment Analysis. This field has some difficulties. The fact that people have complex sentiments is the main cause of the difficulty, but this has not prevented the progress of the studies in this field. Sentiment analysis is generally used to obtain information about persons by collecting their texts or expressions. Sentiment analysis can sometimes bring serious benefits. In this study, with singular tag-plural class approach, a binary classification was performed. An LSTM network and several machine learning models were tested. The dataset collected in Turkish, and Stanford Large Movie Reviews datasets were used in this study. Due to the noise in the dataset, the Zemberek NLP Library for Turkic Languages and Regular Expression techniques were used to normalize and clean texts, later, the data were transformed into vector sequences. The preprocessing process made 2% increase to the model performance on the Turkish Customer Reviews dataset. The model was established using an LSTM network. Our model showed better performance than Machine Learning techniques and achieved an accuracy of 90.59% on the Turkish dataset and an accuracy of 89.02% on the IMDB dataset.
dc.identifier.doi10.2339/politeknik.844019
dc.identifier.endpage966
dc.identifier.issn1302-0900
dc.identifier.issn2147-9429
dc.identifier.issue3
dc.identifier.startpage959
dc.identifier.trdizinid1236230
dc.identifier.urihttps://doi.org/10.2339/politeknik.844019
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay1236230
dc.identifier.urihttps://hdl.handle.net/20.500.12587/23869
dc.identifier.volume25
dc.identifier.wosWOS:000999800700004
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.publisherGazi Univ
dc.relation.ispartofJournal of Polytechnic-Politeknik Dergisi
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241229
dc.subjectDeep learning; machine learning; sentiment analysis; LSTM; sequence embedding
dc.titleLSTM Network Based Sentiment Analysis for Customer Reviews
dc.typeReview Article

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