A R T I C L E I N F O A B S T R A C T Article history: Received: 31 October, 2019 Accepted: 04 January, 2020 Online: 22 January, 2020 E-mail is one of the media services used at the contact center. The challenge faced by email services is how to handle e-mails that enter large quantities every day efficiently to provide fast and appropriate service to customers. The purpose of this study is to find which method has the best accuracy in classifying emails with four classes. The machine learning models compared in this study are Naive Bayes, SVM, and KNN. The data used in this study are primary data got from one of the contact centers. The NLP technique Stop word removal, Stemming, and feature extraction using TF-IDF and Word2vec also applied to each algorithm to improve accuracy. The results of this study indicate that the SVM model with the Word2vec data feature produces the highest level of accuracy and the lowest level of accuracy produced by the Naive Bayes model using the TF-IDF data feature. The conclusion is that the classification using the word2vec data feature has a better level of accuracy than the classification using the TF-IDF data feature.
Paper
Full text
Classification Model of Contact Center Customers Emails Using Machine Learning
Semantic Scholar · Computer Science · 2020
Abstract
A R T I C L E I N F O A B S T R A C T Article history: Received: 31 October, 2019 Accepted: 04 January, 2020 Online: 22 January, 2020 E-mail is one of the media services used at the contact center. The challenge faced by email services is how to handle e-mails that enter large quantities every day efficiently to provide fast and appropriate service to customers. The purpose of this study is to find which method has the best accuracy in classifying emails with four classes. The machine learning models compared in this study are Naive Bayes, SVM, and KNN. The data used in this study are primary data got from one of the contact centers. The NLP technique Stop word removal, Stemming, and feature extraction using TF-IDF and Word2vec also applied to each algorithm to improve accuracy. The results of this study indicate that the SVM model with the Word2vec data feature produces the highest level of accuracy and the lowest level of accuracy produced by the Naive Bayes model using the TF-IDF data feature. The conclusion is that the classification using the word2vec data feature has a better level of accuracy than the classification using the TF-IDF data feature.