Spam Text Detection using Machine Learning Model
Mahasak Ketcham, Thittaporn Ganokratanaa, Patiyuth Pramkeaw, Narumol Chumuang
Abstract
This research presents a classification between spam and non-spam messages by removing duplicate sentences or words. meaningless word and various marks The data is then classified by machine learning techniques and compared by differences between the data sets. Quantitative transformations were performed on each model to find the most efficient model. which can filter spam messages efficiently and quickly By getting the best comparison results in terms of information. quantitative conversion and model use The experimental results showed that Of all the tests, the model that performed best was Random Forest, with an average accuracy of 97
Topics & Concepts
Computer scienceRandom forestArtificial intelligenceWord (group theory)Bag-of-words modelFilter (signal processing)Machine learningNatural language processingData miningMathematicsComputer visionGeometrySpam and Phishing DetectionText and Document Classification TechnologiesAdvanced Text Analysis Techniques