Categorization of customer complaints in food industry using machine learning approaches

Basit Öğe Kaydı
dc.contributor.authorKılınç, Deniz
dc.contributor.authorDoğan, Onur
dc.contributor.authorBozyiğit, Fatma
dc.date.accessioned2023-03-22T19:48:08Z
dc.date.available2023-03-22T19:48:08Z
dc.date.issued2022
dc.departmentBelirleneceken_US
dc.description.abstractCustomer feedback is one of the most critical parameters that determine the market dynamics of product development. In this direction, analyzing product-related complaints helps sellers to identify the quality characteristics and consumer focus. There have been many studies conducted on the design of Machine Learning (ML) systems to address the causes of customer dissatisfaction. However, most of the research has been particularly performed on English. This paper contributes to developing an accurate categorization of customer complaints about package food products, written in Turkish. Accordingly, various ML algorithms using TF-IDF and word2vec feature representation strategies were performed to determine the category of complaints. Corresponding results of Linear Regression (LR), Naive Bayes (NB), k Nearest Neighbour (kNN), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) classifiers were provided in related sections. Experimental results show that the best-performing method is XGBoost with TF-IDF weighting scheme and it achieves %86 F-measure score. The other considerable point is word2vec based ML classifiers show poor performance in terms of F-measure compared to the TF-IDF term weighting scheme. It is also observed that each experimented TF-IDF based ML algorithm gives a more successful prediction performance on the optimal subsets of features selected by the Chi Square (CH2) method. Performing CH2 on TF-IDF features increases the F-measure score from 86% to 88% in XGBoost.en_US
dc.identifier.doi10.38016/jista.954098
dc.identifier.endpage91en_US
dc.identifier.issn2651-3927
dc.identifier.issue1en_US
dc.identifier.startpage85en_US
dc.identifier.trdizinid507883en_US
dc.identifier.urihttps://hdl.handle.net/20.500.14034/989
dc.identifier.urihttps://doi.org/10.38016/jista.954098
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/507883
dc.identifier.volume5en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.relation.journalZeki sistemler teori ve uygulamaları dergisi (Online)en_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectfood industryen_US
dc.subjectcustomer complaintsen_US
dc.subjectcomplaint categorizationen_US
dc.titleCategorization of customer complaints in food industry using machine learning approachesen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
Categorization of customer complaints in food industry using machine learning approaches.pdf
Boyut:
281.73 KB
Biçim:
Adobe Portable Document Format
Açıklama:
İndir

Koleksiyon

TR-Dizin İndeksli Yayınlar Koleksiyonu
Bilgisayar Mühendisliği Bölümü Koleksiyonu
Endüstri Mühendisliği Bölümü Koleksiyonu