Use of Natural Language Processing to Identify Inappropriate Content in Text

  1. Sergio Merayo-Alba 1
  2. Eduardo Fidalgo 1
  3. Victor González-Castro 1
  4. Rocío Alaiz-Rodríguez 1
  5. Javier Velasco-Mata 1
  1. 1 Universidad de León
    info

    Universidad de León

    León, España

    ROR https://ror.org/02tzt0b78

Libro:
Hybrid Artificial Intelligent Systems. 14th International Conference, HAIS 2019: León, Spain, September 4–6, 2019. Proceedings
  1. Hilde Pérez García (coord.)
  2. Lidia Sánchez González (coord.)
  3. Manuel Castejón Limas (coord.)
  4. Héctor Quintián Pardo (coord.)
  5. Emilio Corchado Rodríguez (coord.)

Editorial: Springer Suiza

ISBN: 978-3-030-29859-3,  978-3-030-29858-6

Año de publicación: 2019

Páginas: 254-263

Congreso: Hybrid Artificial Intelligent Systems (14. 2019. León)

Tipo: Aportación congreso

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Resumen

The quick development of communication through new technology media such as social networks and mobile phones has improved our lives. However, this also produces collateral problems such as the presence of insults and abusive comments. In this work, we address the problem of detecting violent content on text documents using Natural Language Processing techniques. Following an approach based on Machine Learning techniques, we have trained six models resulting from the combinations of two text encoders, Term Frequency-Inverse Document Frequency and Bag of Words, together with three classifiers: Logistic Regression, Support Vector Machines and Na¨ıve Bayes. We have also assessed StarSpace, a Deep Learning approach proposed by Facebook and configured to use a Hit@1 accuracy. We evaluated these seven alternatives in two publicly available datasets from the Wikipedia Detox Project: Attack and Aggression. StarSpace achieved an accuracy of 0.938 and 0.937 in these datasets, respectively, being the algorithm recommended to detect violent content on text documents among the alternatives evaluated.

Fuente de los datos: Dialnet