期刊
NEURAL COMPUTING & APPLICATIONS
卷 33, 期 6, 页码 1881-1902出版社
SPRINGER LONDON LTD
DOI: 10.1007/s00521-020-05058-4
关键词
E-mail classification; Machine learning; Long short-term memory; Natural language processing
资金
- Blekinge Institute of Technology
Classifying emails into distinct labels using machine learning can improve customer support efficiency. This study concludes that long short-term memory networks outperform other models in predicting email labels and presents a Web-based interface for classifying emails into 33 different labels.
Classifying e-mails into distinct labels can have a great impact on customer support. By using machine learning to label e-mails, the system can set up queues containing e-mails of a specific category. This enables support personnel to handle request quicker and more easily by selecting a queue that match their expertise. This study aims to improve a manually defined rule-based algorithm, currently implemented at a large telecom company, by using machine learning. The proposed model should have higher F-1-score and classification rate. Integrating or migrating from a manually defined rule-based model to a machine learning model should also reduce the administrative and maintenance work. It should also make the model more flexible. By using the frameworks, TensorFlow, Scikit-learn and Gensim, the authors conduct a number of experiments to test the performance of several common machine learning algorithms, text-representations, word embeddings to investigate how they work together. A long short-term memory network showed best classification performance with an F-1-score of 0.91. The authors conclude that long short-term memory networks outperform other non-sequential models such as support vector machines and AdaBoost when predicting labels for e-mails. Further, the study also presents a Web-based interface that were implemented around the LSTM network, which can classify e-mails into 33 different labels.
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