Categorization of Customer Complaints in Food Industry Using Machine Learning Approaches

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 (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.

Machine Learning-Based Text Classification Comparison: Turkish Language Context

“…The integration approach of the SVM with LR generated the best results (82.9%) [32]. Bozyigit et al [23] deployed ML to classify customers' concerns regarding packaged food goods expressed in Turkish. The class of concern was determined using the TF-IDF and word2vec feature extraction algorithms.…”

“…The results of the LR, NB, K-NN, SVM, RF, and Extreme Gradient Boosting classifiers were compared. The strongest technique was Extreme Gradient Boosting with an TF-IDF weighted value, which reaches an 86% F-measure score [23]. When compared to the TF-IDF method, word2vec-based ML performed poorly in terms of the F-measure.…”

“…When compared to the TF-IDF method, word2vec-based ML performed poorly in terms of the F-measure. Furthermore, TF-IDF-based ML provides more accurate predictions on the optimized feature subsets determined by the Chi-Square approach, which, when conducted on TF-IDF features, raises the F-measure from 86% to 88% in Extreme Gradient Boosting [23].…”

“…The dataset was trained using the SVM, NB, LTSM, RF, and LR methods. This study can be assumed to be an extension and validation of [21][22][23]. The effectiveness of the various strategies was then evaluated both after and before data preparation.…”

“…Another concern to consider is which ML classification algorithm is best for this task. The classification method is challenging in this study as all of the inquiries are in the Turkish composable language [22,23].…”