A Supervised Multi-class Multi-label Word Embeddings Approach for Toxic Comment Classification

Salvatore Carta, Andrea Corriga, Riccardo Mulas, Diego Reforgiato Recupero, Roberto Saia

2019

Abstract

Nowadays, communications made by using the modern Internet-based opportunities have revolutionized the way people exchange information, allowing real-time discussions among a huge number of users. However, the advantages offered by such powerful instruments of communication are sometimes jeopardized by the dangers related to personal attacks that lead many people to leave a discussion that they were participating. Such a problem is related to the so-called toxic comments, i.e., personal attacks, verbal bullying and, more generally, an aggressive way in which many people participate in a discussion, which brings some participants to abandon it. By exploiting the Apache Spark big data framework and several word embeddings, this paper presents an approach able to operate a multi-class multi-label classification of a discussion within a range of six classes of toxicity. We evaluate such an approach by classifying a dataset of comments taken from the Wikipedia’s talk page, according to a Kaggle challenge. The experimental results prove that, through the adoption of different sets of word embeddings, our supervised approach outperforms the state-of-the-art that operate by exploiting the canonical bag-of-word model. In addition, the adoption of a word embeddings defined in a similar scenario (i.e., discussions related to e-learning videos), proves that it is possible to improve the performance with respect to solutions employing state-of-the-art word embeddings.

Paper Citation

in Harvard Style

Carta S., Corriga A., Mulas R., Recupero D. and Saia R. (2019). A Supervised Multi-class Multi-label Word Embeddings Approach for Toxic Comment Classification. In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 1: KDIR; ISBN 978-989-758-382-7, SciTePress, pages 105-112. DOI: 10.5220/0008110901050112

in Bibtex Style

@conference{kdir19,
author={Salvatore Carta and Andrea Corriga and Riccardo Mulas and Diego Reforgiato Recupero and Roberto Saia},
title={A Supervised Multi-class Multi-label Word Embeddings Approach for Toxic Comment Classification},
booktitle={Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 1: KDIR},
year={2019},
pages={105-112},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008110901050112},
isbn={978-989-758-382-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 1: KDIR
TI - A Supervised Multi-class Multi-label Word Embeddings Approach for Toxic Comment Classification
SN - 978-989-758-382-7
AU - Carta S.
AU - Corriga A.
AU - Mulas R.
AU - Recupero D.
AU - Saia R.
PY - 2019
SP - 105
EP - 112
DO - 10.5220/0008110901050112
PB - SciTePress