Performance Analysis of Supervised Machine Learning Algorithms for Text Classification

2025-08-31

AI Summary

This study addresses the comparative performance evaluation of supervised machine learning models for multiclass text classification. We propose and implement an independent, reproducible text classification benchmarking platform that systematically evaluates artificial neural networks (ANNs), backpropagation networks (BPNs), and classical classifiers—including support vector machines (SVM), random forests, and naive Bayes—on a unified, manually annotated dataset. Experiments follow standardized preprocessing, k-fold cross-validation, and accuracy as the primary evaluation metric. Results reveal substantial performance variation across algorithms on real-world text data, with optimal model selection strongly dependent on dataset characteristics. The principal contribution is a scalable, open benchmarking framework for text classification; empirical findings demonstrate that ANNs consistently achieve superior classification accuracy across most test scenarios, thereby providing data-driven guidance for model selection in practical text classification tasks.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Natural Language Processing: Text Classification & Sentiment Analysis
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

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.

Problem

• Analyzing performance of supervised ML algorithms for text classification
• Comparing classifier accuracy on labeled text documents
• Evaluating Neural Network models for supervised text categorization

Innovation

• Supervised machine learning for text classification
• Artificial Neural Network with Back Propagation
• Performance analysis using labeled benchmark datasets