Categorization of Customer Complaints in Food Industry Using Machine Learning Approaches

Authors : Fatma BOZYİĞİT, Onur DOĞAN, Deniz KILINÇ

Pages : 85-91

Doi:10.38016/jista.954098

View : 45 | Download : 10

Publication Date : 2022-03-02

Article Type : Research Paper

Abstract :Customer 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 insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(LR);, Naive Bayes insert ignore into journalissuearticles values(NB);, k Nearest Neighbour insert ignore into journalissuearticles values(kNN);, Support Vector Machine insert ignore into journalissuearticles values(SVM);, Random Forest insert ignore into journalissuearticles values(RF);, and Extreme Gradient Boosting insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(CH2); method. Performing CH2 on TF-IDF features increases the F-measure score from 86% to 88% in XGBoost.
Keywords : customer complaints, complaint categorization, food industry, machine learning

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