A comprehensive review on text classification and text mining techniques using spam dataset detection

Abstract

Text data mining techniques are an essential tool for dealing with raw text data (future fortune). The Text data mining process of securing exceptional knowledge and information from the unstructured text is a fundamental principle of Text data mining to facilitate relevant insights by analyzing a huge volume of raw data in association with Artificial Intelligence natural language processing NLP Machine Learning algorithms. The salient features of text data mining are attracted by the contemporary business applications to have their extraordinary benefits in global area operations. In this, a brief review of text mining techniques, such as clustering, information extraction, text preprocessing, information retrieval, text classification, and text mining applications, that demonstrate the significance of text mining, the predominant text mining techniques, and the predominant contemporary applications that are using text mining. This review includes various existing algorithms, text feature extractions, compression methods, and evaluation techniques. Finally, we used a spam dataset for classification detection data and a three classifier algorithm with TF-IDF feature extraction and through that model achieved higher accuracy with Naïve Bayes. Illustrations of text classification as an application in areas such as medicine, law, education, etc., are also presented.