Abstract:
Social spam has rapidly increased over recent years. This kind of spam contents like text
messaging or comments has a massive negative influence on normal user’s experience in
social media. Our research reflects the current experimental study on spam detection from
different textual data. In this experimental research, we have used Naïve Bayes classifier,
a supervised machine learning algorithm with feature extraction to detect spam from
Bangla text at the sentence level. We have started this research by collecting Bangla textual
data from YouTube, Facebook, and other social media. Then we have categorized the
sentences into two polarities i.e. spam and ham applied to the Multinomial Naïve Bayes
classification algorithm. Our proposed system detects spam on the basis of the polarity of
each sentence associated with it. Finally, our experiment shows that the model has an
accuracy of 82.44% in detecting spam Bangla text content.