SciELO - Scientific Electronic Library Online

 
vol.24 issue2Weighted Bidirectional Graph-based Academic Curricula Model to Support the Tutorial CompetenceAn Univariable Approach for Forecasting Workload in the Maintenance Industry author indexsubject indexsearch form
Home Pagealphabetic serial listing  

Services on Demand

Journal

Article

Indicators

Related links

  • Have no similar articlesSimilars in SciELO

Share


Computación y Sistemas

On-line version ISSN 2007-9737Print version ISSN 1405-5546

Abstract

FRENDA, Simona; BANERJEE, Somnath; ROSSO, Paolo  and  PATTI, Viviana. Do Linguistic Features Help Deep Learning? The Case of Aggressiveness in Mexican Tweets. Comp. y Sist. [online]. 2020, vol.24, n.2, pp.633-643.  Epub Oct 04, 2021. ISSN 2007-9737.  https://doi.org/10.13053/cys-24-2-3398.

In the last years, the control of online user generated content is becoming a priority, because of the increase of online aggressiveness and hate speech legal cases. Considering the complexity and the importance of this issue, this paper presents an approach that combines the deep learning framework with linguistic features for the recognition of aggressiveness in Mexican tweets. This approach has been evaluated relying on a collection of tweets released by the organizers of the shared task about aggressiveness detection in the context of the Ibereval 2018 evaluation campaign. The use of a benchmark corpus allows to compare the results with those obtained by Ibereval 2018 participant systems. However, looking at the achieved results, linguistic features seem not to help the deep learning classification for this task.

Keywords : Deep learning; aggressiveness automatic detection; Mexican Spanish language; twitter; linguistic analysis.

        · text in English