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.

/pdf/a-comparison-of-machine-learning-models-to-prioritise-emails-1zf7m9ou.pdf

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

Use of Natural Language Processing in Software Requirements Prioritization – A Systematic Literature Review

Email has remained the main means of business communication in a school context. It plays a vital role in a school-based communication because it makes it possible for different clients and stakeholders in the educational community to formally communicate effectively, securely, and with documentation, thus the main goal of this research is to classify school-based email data into two categories which is commonly encountered in the records office of a university: General Inquiry Type and Verification Type. The email labeling is done through the use of a rule-based technique that incorporates word embedding, as well as the integration of dictionary-based and user-defined keywords. The study gives emphasis on the use of the dataset of 21,280 emails originating from the researcher's affiliated university. Several deep learning models were employed using Global Vector (GloVe) word embedding and Term Frequency Inverse Document Frequency (TF-IDF) based vectorization. The performance of the deep learning techniques using Word Embeddings on a school-based email data were evaluated based on accuracy, precision, recall, and F1 score. The Convolutional Neural Networks (CNN) model outperformed the other deep learning models which consistently demonstrated the highest results in all metrics used and have a high accuracy of 98% with TF-IDF and 88% with GloVe word embedding. Further, the deep learning models employed are limited only to Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM).

Deep Learning Approaches to a School-based Email Classification with TF-IDF-based vectorization

: Business field has improved exponentially during the last two decades: working methods have changed, more and more users are connected to each other across the globe, same teams as well as different teams can be separated by countries in big companies. So, users need a way to select messages to treat in priority for a better business management and a better communication. In this paper, we implement an approach enabling to classify voicemail messages into urgent and non urgent. The problem of determining urgency being still vast and open, some criteria should be used to decide the importance of messages depending to one’s necessity. Among these criteria, we can mention the sender position, the time of sending as well as the textual content. In this paper, we focus on classifying voicemail messages based on their contents. As there exist several Machine Learning approaches for text vectorization and classification, various combinations will be discussed and compared for the aim of finding the most performant one.

Voicemail Urgency Detection Using Context Dependent and Independent NLP Techniques

Personal and business users prefer to use e-mail as one of the crucial sources of communication. The usage and importance of e-mails continuously grow despite the prevalence of alternative means, such as electronic messages, mobile applications, and social networks. Finding out the important mails of the all the mails received on the same day is becoming difficult for any users, as the volume of critical e-mails continues to grow, the need to automate the management of e-mails increases for several reasons, such as spam e-mail classification, phishing e-mail classification, and multi-folder categorization, among others. To achieve the objective of study, analysis and comprehensive review to explore the classification as per the importance of emails as user need to look these mails. Main area of classification is the important mail folder and categorizing it into different classification by using Naive Bayes algorithm. By Giving the weights to words present in mails we can find out the total weight of that mails and by comparing the weights with the other ones we can prioritize the mails so that it will be efficient to users to look for the important mails only. The research directions, research challenges, and open issues in the field of e-mail classification are also presented for future researchers.

Email Prioritization Using Machine Learning

Personal and business users prefer to use email as one of the crucial sources of communication. The usage and importance of e-mails continuously grow despite the prevalence of alternative means, such as electronic messages, mobile applications, and social networks. Finding out the important mails of the all the emails received on the same day is becoming difficult for many users, as the volume of critical emails continues to grow, the need to automate the management of emails increases for several reasons, such as spam e-mail classification, phishing e-mail classification, and multi-folder categorization, among others. To achieve the objective of study, analysis and comprehensive review to explore the classification as per the importance of emails as users need to look at these mails. The main area of classification is the primary inbox which contains some of the very important mails so to prioritize these mails we are using Natural Language Processing, here we are removing the stop-words present in it and assigning weights to the remaining words. By using frequency count, weight, and access time we can prioritize the mails so that it will be efficient for users to look for the important mails only. The research directions, research challenges, and open issues in the field of e-mail classification are also presented for future researchers.

https://www.jetir.org/papers/JETIR2004268.pdf

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Email Prioritization Using Machine Learning

Email Prioritization Using Machine Learning