Exploring the Relationship Between Algorithm Performance, Vocabulary, and Run-Time in Text Classification

TL;DR

This study investigates how different preprocessing techniques impact vocabulary size, model accuracy, and run-time in text classification, revealing methods that optimize efficiency without sacrificing accuracy.

Contribution

It provides a comprehensive analysis of how preprocessing methods affect run-time and accuracy, highlighting optimal combinations for efficiency in text classification.

Findings

Some preprocessing methods reduce run-time without accuracy loss.

Certain combinations trade minor accuracy for significant run-time reduction.

Some techniques improve both run-time and accuracy simultaneously.

Abstract

Text classification is a significant branch of natural language processing, and has many applications including document classification and sentiment analysis. Unsurprisingly, those who do text classification are concerned with the run-time of their algorithms, many of which depend on the size of the corpus' vocabulary due to their bag-of-words representation. Although many studies have examined the effect of preprocessing techniques on vocabulary size and accuracy, none have examined how these methods affect a model's run-time. To fill this gap, we provide a comprehensive study that examines how preprocessing techniques affect the vocabulary size, model performance, and model run-time, evaluating ten techniques over four models and two datasets. We show that some individual methods can reduce run-time with no loss of accuracy, while some combinations of methods can trade 2-5% of the accuracy for up to a 65% reduction of run-time. Furthermore, some combinations of preprocessing techniques can even provide a 15% reduction in run-time while simultaneously improving model accuracy.