Detecting Aggressiveness in Tweets: A Hybrid Model for Detecting Cyberbullying in the Spanish Language

In recent years, the use of social networks has increased exponentially, which has led to a significant increase in cyberbullying. Currently, in the field of Computer Science, research has been made on how to detect aggressiveness in texts, which is a prelude to detecting cyberbullying. In this fiel...

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Autores principales:	Manuel Lepe-Faúndez, Alejandra Segura-Navarrete, Christian Vidal-Castro, Claudia Martínez-Araneda, Clemente Rubio-Manzano
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	cyberbullying detect emotions analysis in Spanish hybrid approach Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/ff23203be3f346a5b6bd36acb562591f
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spelling	oai:doaj.org-article:ff23203be3f346a5b6bd36acb562591f2021-11-25T16:35:49ZDetecting Aggressiveness in Tweets: A Hybrid Model for Detecting Cyberbullying in the Spanish Language10.3390/app1122107062076-3417https://doaj.org/article/ff23203be3f346a5b6bd36acb562591f2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10706https://doaj.org/toc/2076-3417In recent years, the use of social networks has increased exponentially, which has led to a significant increase in cyberbullying. Currently, in the field of Computer Science, research has been made on how to detect aggressiveness in texts, which is a prelude to detecting cyberbullying. In this field, the main work has been done for English language texts, mainly using Machine Learning (ML) approaches, Lexicon approaches to a lesser extent, and very few works using hybrid approaches. In these, Lexicons and Machine Learning algorithms are used, such as counting the number of bad words in a sentence using a Lexicon of bad words, which serves as an input feature for classification algorithms. This research aims at contributing towards detecting aggressiveness in Spanish language texts by creating different models that combine the Lexicons and ML approach. Twenty-two models that combine techniques and algorithms from both approaches are proposed, and for their application, certain hyperparameters are adjusted in the training datasets of the corpora, to obtain the best results in the test datasets. Three Spanish language corpora are used in the evaluation: Chilean, Mexican, and Chilean-Mexican corpora. The results indicate that hybrid models obtain the best results in the 3 corpora, over implemented models that do not use Lexicons. This shows that by mixing approaches, aggressiveness detection improves. Finally, a web application is developed that gives applicability to each model by classifying tweets, allowing evaluating the performance of models with external corpus and receiving feedback on the prediction of each one for future research. In addition, an API is available that can be integrated into technological tools for parental control, online plugins for writing analysis in social networks, and educational tools, among others.Manuel Lepe-FaúndezAlejandra Segura-NavarreteChristian Vidal-CastroClaudia Martínez-AranedaClemente Rubio-ManzanoMDPI AGarticlecyberbullying detectemotions analysis in Spanishhybrid approachTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10706, p 10706 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	cyberbullying detect emotions analysis in Spanish hybrid approach Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	cyberbullying detect emotions analysis in Spanish hybrid approach Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Manuel Lepe-Faúndez Alejandra Segura-Navarrete Christian Vidal-Castro Claudia Martínez-Araneda Clemente Rubio-Manzano Detecting Aggressiveness in Tweets: A Hybrid Model for Detecting Cyberbullying in the Spanish Language
description	In recent years, the use of social networks has increased exponentially, which has led to a significant increase in cyberbullying. Currently, in the field of Computer Science, research has been made on how to detect aggressiveness in texts, which is a prelude to detecting cyberbullying. In this field, the main work has been done for English language texts, mainly using Machine Learning (ML) approaches, Lexicon approaches to a lesser extent, and very few works using hybrid approaches. In these, Lexicons and Machine Learning algorithms are used, such as counting the number of bad words in a sentence using a Lexicon of bad words, which serves as an input feature for classification algorithms. This research aims at contributing towards detecting aggressiveness in Spanish language texts by creating different models that combine the Lexicons and ML approach. Twenty-two models that combine techniques and algorithms from both approaches are proposed, and for their application, certain hyperparameters are adjusted in the training datasets of the corpora, to obtain the best results in the test datasets. Three Spanish language corpora are used in the evaluation: Chilean, Mexican, and Chilean-Mexican corpora. The results indicate that hybrid models obtain the best results in the 3 corpora, over implemented models that do not use Lexicons. This shows that by mixing approaches, aggressiveness detection improves. Finally, a web application is developed that gives applicability to each model by classifying tweets, allowing evaluating the performance of models with external corpus and receiving feedback on the prediction of each one for future research. In addition, an API is available that can be integrated into technological tools for parental control, online plugins for writing analysis in social networks, and educational tools, among others.
format	article
author	Manuel Lepe-Faúndez Alejandra Segura-Navarrete Christian Vidal-Castro Claudia Martínez-Araneda Clemente Rubio-Manzano
author_facet	Manuel Lepe-Faúndez Alejandra Segura-Navarrete Christian Vidal-Castro Claudia Martínez-Araneda Clemente Rubio-Manzano
author_sort	Manuel Lepe-Faúndez
title	Detecting Aggressiveness in Tweets: A Hybrid Model for Detecting Cyberbullying in the Spanish Language
title_short	Detecting Aggressiveness in Tweets: A Hybrid Model for Detecting Cyberbullying in the Spanish Language
title_full	Detecting Aggressiveness in Tweets: A Hybrid Model for Detecting Cyberbullying in the Spanish Language
title_fullStr	Detecting Aggressiveness in Tweets: A Hybrid Model for Detecting Cyberbullying in the Spanish Language
title_full_unstemmed	Detecting Aggressiveness in Tweets: A Hybrid Model for Detecting Cyberbullying in the Spanish Language
title_sort	detecting aggressiveness in tweets: a hybrid model for detecting cyberbullying in the spanish language
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ff23203be3f346a5b6bd36acb562591f
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Detecting Aggressiveness in Tweets: A Hybrid Model for Detecting Cyberbullying in the Spanish Language

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