SMS Spam Detection using Tokenization and Feature Engineering
Akshay Divakar1, Sitaraa Krishnakumar2

1Akshay Divakar Student Institute : SRM Institute of Science and Technology Chennai Tamil Nadu India
2Sitara Krishnakumar, Student Institute : SRM Institute of Science and Technology Chennai Tamil Nadu.

Manuscript received on 07 August 2019. | Revised Manuscript received on 12 August 2019. | Manuscript published on 30 September 2019. | PP: 6805-6807 | Volume-8 Issue-3 September 2019 | Retrieval Number: C5736098319/2019©BEIESP | DOI: 10.35940/ijrte.C5736.098319
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Abstract: The enormous development of innovation and mobiles, the clients have been exposed to more spam messages than any other time in recent memory ever. SMS spam separating is a nearly an exceptionally ongoing answer for arrangement with such a significant issue.. This paper moves us to chip away at the assignment of separating versatile spam messages as whether it is Ham or Spam for the clients by adding messages to the worldwide accessible SMS dataset. The paper plans to break down various AI classifiers on huge corpus of SMS messages for the individuals around the globe. This paper also informs or tells the readers about the existing algorithms and it’s inefficiency in filtering the ham messages from spam messages. This paper makes use of tokenization to create tokens which are then fed into the feature engineering model to extract features and then to predict the outcome.
Keywords— Mobile Phone Spam; SMS Spam; Spam

Scope of the Article:
Mobile Agents