A Sentiment Analysis Benchmark for Automated Machine Learning Applications

Developing new applied Artificial Intelligence solutions can be complex and time-consuming. Automated Machine Learning (AutoML) can speed up the solutions' development, but may require hundreds of datasets to make the learning process viable, which may be a challenge for Natural Language Processing (NLP) tasks, as many of them need annotated datasets that are expensive to produce. It may also be necessary to artificially recombine classes to generate new datasets for Meta-Learning (MtL), which can be considered as a particular AutoML approach. This paper addresses such challenge and proposes an AutoML benchmark for sentiment analysis, which comprises relevant tasks in NLP, resulting in 49 preprocessed datasets, more than 650 binary datasets, and 204 meta-features. More than this, a proof of concept is carried out for the hate speech detection task.