Email Spams via Text Mining using Machine Learning Techniques

Abstract

A lot of data is generated on daily basis which may potentially be useful. This data is generally unstructured and ambiguous to draw a meaning from it. High quality of information can be extracted from this potentially useful data typically through devising of patterns and trends in it. This is done using Text Mining which includes the initial parsing of the unstructured data, processing it and then leading to some meaningful and fascinating information hidden in it. This paper presents the machine learning techniques for text mining that are useful for spam detection in emails.

Text Mining: Techniques, Applications and Issues

“…Today, it is difficult to categorize manually all digital documents available in the digital world due to their huge size. So a technique, which is based on text mining, machine learning and artificial intelligence, is used to categorize these documents automatically called document classification [18]. Document classification techniques assigns categories or classes to digital documents, which makes them easier to search, filter, manage and analyze.…”

“…A machine learning model based on text mining is used to solve this problem to categorize the email as a spam or nonspam. It is also an important objective of spam detection is to extract the way by which an email is classified as spam and used it for the purpose of categorize the mail in future [18][4] [10].…”

“…To overcome this problem, text mining may be applied with NLP along with fuzzy set theory and knowledge regarding the context to lexical semantics [7] [16].Noise in the text may be like typo error, spelling mistakes and use of non-standard abbreviations. To manage noise in text, use of spellchecking application and regular expression make clean and normalize data before applying text mining [18], . Availability of text document in different language is another big challenge to extract information from multilingual datasets during text mining.…”