# Leveraging Automated Machine Learning for Text Classification: Evaluation of AutoML Tools and Comparison with Human Performance

arXiv: https://arxiv.org/abs/2012.03575 · HTML: https://mlsanity.com/paper/2012.03575/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2020.

## Built on by (most cited first, 4 of 4)

- [Benchmarking Multimodal AutoML for Tabular Data with Text Fields](https://mlsanity.com/paper/2111.02705/) (2021)
- [SentimentArcs: A Novel Method for Self-Supervised Sentiment Analysis of Time Series Shows SOTA Transformers Can Struggle Finding Narrative Arcs](https://mlsanity.com/paper/2110.09454/) (2021)
- [A Robust Stacking Framework for Training Deep Graph Models with Multifaceted Node Features](https://mlsanity.com/paper/2206.08473/) (2022)
- [Evaluation of Representation Models for Text Classification with AutoML Tools](https://mlsanity.com/paper/2106.12798/) (2021)

## Abstract (arXiv)

Recently, Automated Machine Learning (AutoML) has registered increasing success with respect to tabular data. However, the question arises whether AutoML can also be applied effectively to text classification tasks. This work compares four AutoML tools on 13 different popular datasets, including Kaggle competitions, and opposes human performance. The results show that the AutoML tools perform better than the machine learning community in 4 out of 13 tasks and that two stand out.