Sel, IlhamiHanbay, Davut2024-08-042024-08-042019978-1-7281-1904-52165-0608https://doi.org/10.1109/siu.2019.8806593https://hdl.handle.net/11616/9894827th Signal Processing and Communications Applications Conference (SIU) -- APR 24-26, 2019 -- Sivas Cumhuriyet Univ, Sivas, TURKEYThanks to the rapid increase in technology and electronic communications, e-mail has become a serious communication tool. In many applications such as business correspondence, reminders, academic notices, web page memberships, e-mail is used as primary way of communication. If we ignore spam e-mails, there remain hundreds of e-mails received every day. In order to determine the importance of received e-mails, the subject or content of each e-mail must be checked. In this study we proposed an unsupervised system to classify received e-mails. Received e-mails' coordinates are determined by a method of natural language processing called as Word2Vec algorithm. According to the similarities, processed data are grouped by k-means algorithm with an unsupervised training model. In this study, 10517 e-mails were used in training. The success of the system is tested on a test group of 200 e-mails. In the test phase M3 model (window size 3, min. Word frequency 10, Gram skip) consolidated the highest success (91%). Obtained results are evaluated in section VI.trinfo:eu-repo/semantics/closedAccessNatural Language ProcessingText ClassificationWord2VecSkip GramK-meansConference Object10.1109/siu.2019.88065932-s2.0-85071993162N/AWOS:000518994300229N/A