SMART DOCUMENT CLASSIFICATION

Abstract

Smart document classification using Naive Bayes is a method used for organizing large volumes of unstructured data into categories or classes. This method uses the Naive Bayes algorithm's probabilistic approach to determine the likelihood that a text falls into a specific category based on the frequency of its words. A machine learning approach called Naive Bayes is used to forecast the likelihood of a particular category depending on the characteristics of the document. For text classification applications including spam filtering, sentiment analysis, and topic modeling, this method has been frequently employed. In this method, the algorithm is trained on a labeled dataset, which contains a set of documents and their corresponding categories The algorithm can be used to categorize fresh documents into the appropriate categories after it has been trained. The standard and applicability of the features used to represent the documents will determine how accurate the classification is. The Naive Bayes classifier is a popular option for smart document categorization since it is simple to use and needs little training data to attain high accuracy. Overall, automatic document classification using naive Bayes is an effective and efficient approach for organizing and managing large volumes of textual data. Key Words: classification, Naïve Bayes, machine learning, textual data