Machine Learning in Automated Text Categorization

TL;DR

This paper surveys machine learning methods for automated text categorization, highlighting their advantages over manual approaches and discussing key issues in document representation, classifier construction, and evaluation.

Contribution

It provides a comprehensive overview of machine learning techniques applied to text classification, emphasizing their effectiveness and adaptability across domains.

Findings

Machine learning approaches outperform manual classification methods.

Effective document representation is crucial for accurate categorization.

Evaluation metrics are essential for assessing classifier performance.

Abstract

The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert manpower, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely document representation, classifier construction, and classifier evaluation.