Evaluation of Hand-Crafted Features for the Classification of Spam SMS in Dravidian Languages

In this digital era, people are cheated in multiple ways by sending fake messages. Without realizing its impact, they respond to the links the cyber frauds share. This immediate reaction to the fraud messages makes people lose their balance in bank accounts or fall into some other horrible events. These types of fake or spam messages have to be identified earlier before they come to users’ Inbox. This paper proposes a Spam message filtering model that extracts significant hand-crafted features and is classified using machine learning algorithms. This research collects 7700 short messages in Dravidian languages like Tamil, Kannada, Telugu, and Malayalam and creates an optimal Spam-Ham filtering framework. Experimentation has also been carried out with a benchmark dataset for performance comparison regarding accuracy, precision, recall, and F1-score.