E-Mail Data Analysis by Considering Auxiliary Information

Abstract

The immense evolution of the technologies is directly proportional to the rise of mails in our email boxes. Emails are always considered as the best source of communication. To utilize the true potential of these emails (unstructured data) transformation should be done on it in order to extract needed information from it thus saving time. Data mining fulfills this need. Also the main information is carried by the documents attached to these mails, so extraction of this auxiliary data is very necessary. To access these emails effectively with the auxiliary data present in them as per user’s sentiments, this paper propose text analytics method to cluster the mails into different groups on the basis of emotions using various scalable machine learning techniques.