Applying machine learning to assess emotional reactions to video game content streamed on Spanish Twitch channels

This research explores for the first time the application of machine learning to detect emotional responses in video game streaming channels, specifically on Twitch, the most widely used platform for broadcasting content. Analyzing sentiment in gaming contexts is difficult due to the brevity of mess...

Descripción completa

Detalles Bibliográficos
Autores: Merayo, Noemí, Cotelo García, Rosalía, Carratalá-Sáez, Rocío, Andújar, Francisco J.
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universidad Autónoma de Madrid
Repositorio:Biblos-e Archivo. Repositorio Institucional de la UAM
Idioma:inglés
OAI Identifier:oai:repositorio.uam.es:10486/716877
Acceso en línea:http://hdl.handle.net/10486/716877
https://dx.doi.org/10.1016/j.csl.2024.101651
Access Level:acceso abierto
Palabra clave:Corpus
Emotional response
Machine learning
Twitch
Video games
Educación
Filología
Informática
id ES_7bca06f00c890a507d7d18c19108b656
oai_identifier_str oai:repositorio.uam.es:10486/716877
network_acronym_str ES
network_name_str España
repository_id_str
spelling Applying machine learning to assess emotional reactions to video game content streamed on Spanish Twitch channelsMerayo, NoemíCotelo García, RosalíaCarratalá-Sáez, RocíoAndújar, Francisco J.CorpusEmotional responseMachine learningTwitchVideo gamesEducaciónFilologíaInformáticaThis research explores for the first time the application of machine learning to detect emotional responses in video game streaming channels, specifically on Twitch, the most widely used platform for broadcasting content. Analyzing sentiment in gaming contexts is difficult due to the brevity of messages, the lack of context, and the use of informal language, which is exacerbated in the gaming environment by slang, abbreviations, memes, and jargon. First, a novel Spanish corpus was created from chat messages on Spanish video game Twitch channels, manually labeled for polarity and emotions. It is noteworthy as the first Spanish corpus for analyzing social responses on Twitch. Secondly, machine learning algorithms were used to classify polarity and emotions offering promising evaluations. The methodology followed in this work consists of three main steps: (1) Extracting Twitch chat messages from Spanish streamers’ channels related to gaming events and gameplays; (2) Processing and selecting the messages to form the corpus and manually annotating polarity and emotions; and (3) Applying machine learning models to detect polarity and emotions in the created corpus. The results have shown that a Bidirectional Encoder Representation from Transformers (BERT) based model excels with 78% accuracy in polarity detection, while deep learning and Random Forest models reach around 70%. For emotion detection, the BERT model performs best with 68%, followed by deep learning with 55%. It is worth noting that emotion detection is more challenging due to the subjective interpretation of emotions in the complex communicative context of video gaming on platforms such as Twitch. The use of supervised learning techniques, together with the rigorous corpus labeling process and the subsequent corpus pre-processing methodology, has helped to mitigate these challenges, and the algorithms have performed well. The main limitations of the research involve category and video game representation balance. Finally, it is important to stress that the integration of machine learning in video games and on Twitch is innovative, by allowing the identification of viewers’ emotions on streamers’ channels. This innovation could bring benefits such as a better understanding of audience sentiment, improving content and audience retention, providing personalized recommendations and detecting toxic behavior in chatsThis work has been supported by the University of Valladolid, SpainElsevier Ltd.Departamento de Filologías y su DidácticaFacultad de Formación de Profesorado y Educación20242024-11-01research articlehttp://purl.org/coar/resource_type/c_2df8fbb1VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10486/716877https://dx.doi.org/10.1016/j.csl.2024.101651reponame:Biblos-e Archivo. Repositorio Institucional de la UAMinstname:Universidad Autónoma de MadridInglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:repositorio.uam.es:10486/7168772026-06-23T12:46:27Z
dc.title.none.fl_str_mv Applying machine learning to assess emotional reactions to video game content streamed on Spanish Twitch channels
title Applying machine learning to assess emotional reactions to video game content streamed on Spanish Twitch channels
spellingShingle Applying machine learning to assess emotional reactions to video game content streamed on Spanish Twitch channels
Merayo, Noemí
Corpus
Emotional response
Machine learning
Twitch
Video games
Educación
Filología
Informática
title_short Applying machine learning to assess emotional reactions to video game content streamed on Spanish Twitch channels
title_full Applying machine learning to assess emotional reactions to video game content streamed on Spanish Twitch channels
title_fullStr Applying machine learning to assess emotional reactions to video game content streamed on Spanish Twitch channels
title_full_unstemmed Applying machine learning to assess emotional reactions to video game content streamed on Spanish Twitch channels
title_sort Applying machine learning to assess emotional reactions to video game content streamed on Spanish Twitch channels
dc.creator.none.fl_str_mv Merayo, Noemí
Cotelo García, Rosalía
Carratalá-Sáez, Rocío
Andújar, Francisco J.
author Merayo, Noemí
author_facet Merayo, Noemí
Cotelo García, Rosalía
Carratalá-Sáez, Rocío
Andújar, Francisco J.
author_role author
author2 Cotelo García, Rosalía
Carratalá-Sáez, Rocío
Andújar, Francisco J.
author2_role author
author
author
dc.contributor.none.fl_str_mv Departamento de Filologías y su Didáctica
Facultad de Formación de Profesorado y Educación
dc.subject.none.fl_str_mv Corpus
Emotional response
Machine learning
Twitch
Video games
Educación
Filología
Informática
topic Corpus
Emotional response
Machine learning
Twitch
Video games
Educación
Filología
Informática
description This research explores for the first time the application of machine learning to detect emotional responses in video game streaming channels, specifically on Twitch, the most widely used platform for broadcasting content. Analyzing sentiment in gaming contexts is difficult due to the brevity of messages, the lack of context, and the use of informal language, which is exacerbated in the gaming environment by slang, abbreviations, memes, and jargon. First, a novel Spanish corpus was created from chat messages on Spanish video game Twitch channels, manually labeled for polarity and emotions. It is noteworthy as the first Spanish corpus for analyzing social responses on Twitch. Secondly, machine learning algorithms were used to classify polarity and emotions offering promising evaluations. The methodology followed in this work consists of three main steps: (1) Extracting Twitch chat messages from Spanish streamers’ channels related to gaming events and gameplays; (2) Processing and selecting the messages to form the corpus and manually annotating polarity and emotions; and (3) Applying machine learning models to detect polarity and emotions in the created corpus. The results have shown that a Bidirectional Encoder Representation from Transformers (BERT) based model excels with 78% accuracy in polarity detection, while deep learning and Random Forest models reach around 70%. For emotion detection, the BERT model performs best with 68%, followed by deep learning with 55%. It is worth noting that emotion detection is more challenging due to the subjective interpretation of emotions in the complex communicative context of video gaming on platforms such as Twitch. The use of supervised learning techniques, together with the rigorous corpus labeling process and the subsequent corpus pre-processing methodology, has helped to mitigate these challenges, and the algorithms have performed well. The main limitations of the research involve category and video game representation balance. Finally, it is important to stress that the integration of machine learning in video games and on Twitch is innovative, by allowing the identification of viewers’ emotions on streamers’ channels. This innovation could bring benefits such as a better understanding of audience sentiment, improving content and audience retention, providing personalized recommendations and detecting toxic behavior in chats
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-11-01
dc.type.none.fl_str_mv research article
http://purl.org/coar/resource_type/c_2df8fbb1
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10486/716877
https://dx.doi.org/10.1016/j.csl.2024.101651
url http://hdl.handle.net/10486/716877
https://dx.doi.org/10.1016/j.csl.2024.101651
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier Ltd.
publisher.none.fl_str_mv Elsevier Ltd.
dc.source.none.fl_str_mv reponame:Biblos-e Archivo. Repositorio Institucional de la UAM
instname:Universidad Autónoma de Madrid
instname_str Universidad Autónoma de Madrid
reponame_str Biblos-e Archivo. Repositorio Institucional de la UAM
collection Biblos-e Archivo. Repositorio Institucional de la UAM
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869411541292417024
score 15,811543

Similares en LA Referencia