Performance Analysis of Supervised Machine Learning Algorithms for Text Classification
This work addresses the growing need for text classification in fields like web searching and recommendation systems, but it is incremental as it applies existing methods to new data without introducing novel techniques.
The paper compares standard supervised machine learning algorithms for text classification on various datasets, measuring accuracy, and finds that an Artificial Neural Network model with Back Propagation Network performs well in experimental analysis on real data.
The demand for text classification is growing significantly in web searching, data mining, web ranking, recommendation systems, and so many other fields of information and technology. This paper illustrates the text classification process on different datasets using some standard supervised machine learning techniques. Text documents can be classified through various kinds of classifiers. Labeled text documents are used to classify the text in supervised classifications. This paper applies these classifiers on different kinds of labeled documents and measures the accuracy of the classifiers. An Artificial Neural Network (ANN) model using Back Propagation Network (BPN) is used with several other models to create an independent platform for labeled and supervised text classification process. An existing benchmark approach is used to analyze the performance of classification using labeled documents. Experimental analysis on real data reveals which model works well in terms of classification accuracy.