Abstract: Short Message Service (SMS) has been widely exploited in day to day communication. SMS classification is the process of classifying queries into predefined departments. In this paper, we analyse the concept of new classification model which will classify mobile SMS into predefined departments such as examination, sports, admission etc. The main purposed of our system is to extract the answer for the SMS query sent by the student on the college server to fulfil the time quotient and provide best information in lesser time. The ideas introduced by the proposed system are ANN for classification of mobile SMS into predefined classes, feature selection and SVM based text message classification using document frequency threshold, a novel semi- supervised method to detect spam SMSs using frequent item set mining algorithm apriori. The methodologies discussed above had some drawbacks. So, this paper puts forward an idea of mobile SMS classification in which registered users can send query and get appropriate reply.
Keywords: Pre-processing, term weight, TF-IDF, Fuzzy classification.
Title: Mobile SMS Classification
Author: Ruchika Bokey, Najuka Sheth, Rakhi Shende, Sunita Ghare
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
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