Machine Learning Techniques for the Detection of Inappropriate Erotic Content in Text

Authors

Gonzalo Molpeceres Barrientos¹, Rocío Alaiz-Rodríguez^{1, *,} , Víctor González-Castro¹, Andrew C. Parnell^2,

¹Department of Electrical, Systems and Automation Engineering, Universidad de León Campus de Vegazana s/n, León, Spain

²Hamilton Institute, Maynooth University, Maynooth, Ireland

^*Corresponding author. Email: rocio.alaiz@unileon.es

DOI

10.2991/ijcis.d.200519.003How to use a DOI?

Keywords

Inappropriate content; Machine learning; Text classification; Natural language processing; Text encoders

Abstract

Nowadays, children have access to Internet on a regular basis. Just like the real world, the Internet has many unsafe locations where kids may be exposed to inappropriate content in the form of obscene, aggressive, erotic or rude comments. In this work, we address the problem of detecting erotic/sexual content on text documents using Natural Language Processing (NLP) techniques. Following an approach based on Machine Learning techniques, we have assessed twelve models resulting from the combination of three text encoders (Bag of Words, Term Frequency-Inverse Document Frequency and Word2vec) together with four classifiers (Support Vector Machines (SVMs), Logistic Regression, k-Nearest Neighbors and Random Forests). We evaluated these alternatives on a new created dataset extracted from public data on the Reddit Website. The best performance result was achieved by the combination of the text encoder TF-IDF and the SVM classifier with linear kernel with an accuracy of 0.97 and F-score 0.96 (precision 0.96/recall 0.95). This study demonstrates that it is possible to detect erotic content on text documents and therefore, develop filters for minors or according to user's preferences.

Copyright

© 2020 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Gonzalo Molpeceres Barrientos
AU  - Rocío Alaiz-Rodríguez
AU  - Víctor González-Castro
AU  - Andrew C. Parnell
PY  - 2020
DA  - 2020/06/11
TI  - Machine Learning Techniques for the Detection of Inappropriate Erotic Content in Text
JO  - International Journal of Computational Intelligence Systems
SP  - 591
EP  - 603
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200519.003
DO  - 10.2991/ijcis.d.200519.003
ID  - Barrientos2020
ER  -