SMS SPAM DETECTION WITH MULTINOMIAL NAIVE BAYES

SMS SPAM DETECTION WITH MULTINOMIAL NAIVE BAYES

Mrs. J Mounika1, Ms. Pallerla Sai Navya2, Ms. Nadikattu Jayadeepika3,
Ms. Nukala Harsha Vardhani4, Ms. Kolluri Priscilla5

1 Asst. Professor, Dept. Information Technology, KKR & KSR Institute of Technology and Sciences, Guntur, India

2-5Student, Dept. Information Technology, KKR & KSR Institute of Technology and Sciences, Guntur, India

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Abstract - SMS (short messaging service) usage has increased dramatically as a result of the growth in mobile users, enabling text messaging between smartphone and landline users. But there has also been a noticeable increase in unsolicited communications, or spam, coinciding with this growth in SMS usage. Through marketing campaigns and attempts to gain private information, such as credit card numbers, these spam messages seek to further business or financial objectives. The duty of removing spam mails has therefore grown in significance. In response, a number of deep learning and machine learning methods have been used to identify SMS spam. Using data from the University of California, Irvine (UCI), this research examines the application of such strategies.

Key Words: MultinomailNaiveBayes, SMS Spam Detection,

NLTK, Vectorization, Feature Extraction.