Email Prioritization Using Machine Learning

Choudhari, Swapnil; Choudhary, Narayan; Kaware, Sumit; Shaikh, Ahmed

doi:10.2139/ssrn.3568518

Cited by 3 publications

(3 citation statements)

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“…Choudhary S., et al [13], in their work have discussed about email prioritization using Machine Learning algorithms. Naïve Baye's Classifier is used here for the classification of emails.…”

Section: Research Objective and Methodologymentioning

confidence: 99%

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

Vijayakumar

Nethravathi

2021

IJAEML

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Purpose: Research involves the creation and implementation of new ideas by keeping the existing work as a foundation. The literature review done in this paper is to familiarise and to know about the domain of research to integrate the existing ideas with the new ones. Methodology: The literature that is required for this study is chosen from multiple secondary sources such as journals, conference proceedings, and web resources. All the pieces of literature are carefully studied and summarised. This is further used to arrive at Research agendas and Research gaps. Findings/Result: It has been observed and understood that Natural Language Processing (NLP) is a field involving analysis and processing of textual contents. It also requires Machine Learning Algorithms to support the processing. This combination has already been used in various domains, the important one being the health sector. EMR data is huge and NLP can successfully process and prioritize them in different dimensions. In that direction, the same concept and technology can be applied to Software Engineering also and Requirements can be prioritized. Originality: This literature review study is carried out using secondary data which is collected through various online sources. The information thus gathered will be used in the future to build upon existing theory and framework or build a new methodology. It is also seen that any conclusion or decision is not biased or unidirectional. A sincere effort is made to identify a research topic to carry out the research. Paper Type: Literature Review.

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Section: Research Objective and Methodologymentioning

confidence: 99%

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

Vijayakumar

Nethravathi

2021

IJAEML

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“…To the researchers" knowledge, research on using machine learning and emotion analysis for email prioritization is scarce. One such research can be found in [27]. The authors used Naïve Bayes to categorize several emails according to their importance.…”

Section: Introductionmentioning

confidence: 99%

“…The authors used Naïve Bayes to categorize several emails according to their importance. [27] hypothesized that assigning different weights to selected terms from email contents makes it possible to calculate the overall importance or priority of these emails. However, the authors did not report any implementation results.…”

Section: Introductionmentioning

confidence: 99%

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

Chuttur

Parianen

2022

Knowl. Eng. Data Sci.

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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.

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

Adrales,

Sison

2024

2024 IEEE 6th Symposium on Computers &Amp;amp; Informatics (ISCI)

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Email Prioritization Using Machine Learning

Cited by 3 publications

References 0 publications

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

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

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

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

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