# Text Classification Algorithms: A Survey

## TL;DR

This survey reviews various machine learning algorithms for text classification, discussing feature extraction, dimensionality reduction, and evaluation methods, highlighting challenges and real-world applications in natural language processing.

## Contribution

It provides a comprehensive overview of existing text classification algorithms, their techniques, limitations, and practical applications, serving as a valuable resource for researchers.

## Key findings

- Different feature extraction methods impact classification accuracy.
- Dimensionality reduction techniques improve computational efficiency.
- Evaluation methods vary in effectiveness across applications.

## Abstract

In recent years, there has been an exponential growth in the number of complex documents and texts that require a deeper understanding of machine learning methods to be able to accurately classify texts in many applications. Many machine learning approaches have achieved surpassing results in natural language processing. The success of these learning algorithms relies on their capacity to understand complex models and non-linear relationships within data. However, finding suitable structures, architectures, and techniques for text classification is a challenge for researchers. In this paper, a brief overview of text classification algorithms is discussed. This overview covers different text feature extractions, dimensionality reduction methods, existing algorithms and techniques, and evaluations methods. Finally, the limitations of each technique and their application in the real-world problem are discussed.