A Comparison of Machine Learning Models to Prioritise Emails using Emotion Analysis for Customer Service Excellence

Abstract

There has been little research on machine learning for email prioritization for customer service excellence. To fill this gap, we propose and assess the efficacy of various machine learning techniques for classifying emails into three degrees of priority: high, low, and neutral, based on the emotions inherent in the email content. It is predicted that after emails are classified into those three categories, recipients will be able to respond to emails more efficiently and provide better customer service. We use the NRC Emotion Lexicon to construct a labeled email dataset of 517,401 messages for our proposal. Following that, we train and test four prominent machine learning models, MNB, SVM, LogR, and RF, and an Ensemble of MNB, LSVC, and RF classifiers, on the labeled dataset. Our main findings suggest that machine learning may be used to classify emails based on their emotional content. However, some models outperform others. During the testing phase, we also discovered that the LogR and LSVC models performed the best, with an accuracy of 72%, while the MNB classifier performed the poorest. Furthermore, classification performance differed depending on whether the dataset was balanced or imbalanced. We conclude that machine learning models that employ emotions for email classification are a promising avenue that should be explored further.

K-Means Clustering and Multilayer Perceptron for Categorizing Student Business Groups

“…K-means clustering is one of the popular algorithms in data analysis used to group data into different clusters based on similarities in features or attributes [14] [15]. On the other hand, the MLP is one type of structured Artificial Neural Network (ANN) [16] architecture that utilizes supervised learning methods [17], known as backpropagation, for classification purposes [18] [19]. The MLP is chosen because it is highly effective, easy to implement, and provides good results in many cases [20][21] [22].…”