Smart SMS Classification for Android Operating System Using Natural Language Processing

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dc.contributor.authorSumanasekara, S.S.
dc.contributor.authorKaumada, M.W.S.
dc.contributor.authorJayasekara, N.E.C.
dc.contributor.authorWimaladharma, S.T.C.I.
dc.date.accessioned2021-02-01T07:40:31Z
dc.date.available2021-02-01T07:40:31Z
dc.date.issued2020
dc.description.abstractThe use of Short Message Service (SMS) is increasing as more people exchange SMS messages very frequently due to the rapid increase of mobile phone usage and the simplicity in sending SMS messages. However, this has led to an increase in mobile device attacks using SMS Spam. The two main categories of SMS Messages are spam messages and ham (legitimate) messages. Up to now, several kinds of research were done on SMS classification but all of them are on spam filtering techniques by using various algorithms and machine learning techniques. In this paper, we present a novel approach that can detect and filter both spam and ham messages into a better organization under six different predefined categories named as Primary for legitimate messages, Bank and Finance, Social and Web, Promotions, Service Provider Messages, and Spam Messages by using Natural Language Processing for Android Operating System. A smart messaging application that can properly organize SMS into categories will help to identify the SMS easily as they are classified under different tabs. Even though SMS can be identified and categorized manually with little or no effort by people, it remains difficult for mobile phones. A dataset is created according to the Sri Lankan context and various experiments are performed to evaluate the performance of the SMS Classification. Initially, the features were selected based on the behavior of messages and extracted the features from the dataset to get the feature vectors. Naive Bayes and Support Vector Machines algorithms were used to select the best classification algorithm. With the highest accuracy rate, the Support Vector Machines algorithm is selected to train the model while k-Fold cross-validation is used to perform the validation. Our proposed approach achieved a 93% accuracy rate and the model is deployed in the Android environment and its performance is confirmed using a proof of concept. Keywords: SMS classification, Natural language processing, Support vector machines, Naive bayes algorithm, Androiden_US
dc.identifier.isbn9789550481293
dc.identifier.urihttp://www.erepo.lib.uwu.ac.lk/bitstream/handle/123456789/5724/proceeding_oct_08-201.pdf?sequence=1&isAllowed=y
dc.language.isoenen_US
dc.publisherUva Wellassa University of Sri Lankaen_US
dc.relation.ispartofseries;International Research Conference
dc.subjectComputer Scienceen_US
dc.subjectInformation Scienceen_US
dc.subjectComputing and Information Managementen_US
dc.subjectTelecommunicationen_US
dc.titleSmart SMS Classification for Android Operating System Using Natural Language Processingen_US
dc.title.alternativeInternational Research Conference 2020en_US
dc.typeOtheren_US
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