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.
Abstract (translated)
文本分类的需求在网页搜索、数据挖掘、网页排名、推荐系统以及其他许多信息和技术领域中正显著增长。本文通过使用一些标准的监督机器学习技术，在不同的数据集上展示了文本分类的过程。可以通过多种类型的分类器对文本文件进行分类。在监督分类中，带有标签的文本文档被用来分类文本。本文将这些分类器应用于不同种类的标记文档，并衡量这些分类器的准确性。 使用人工神经网络（ANN）模型和反向传播网络（BPN），与其他几种模型一起构建了一个独立平台，用于对带标签和监督下的文本分类过程进行处理。利用现有的基准方法来分析使用标记文档时分类性能的表现。通过真实数据上的实验分析揭示了哪种模型在分类准确性方面表现良好。
URL
https://arxiv.org/abs/2509.00983
