Towards the Understanding of Gaming Audiences by Modeling Twitch Emotes

NUT@EMNLP Pub Date : 2017-09-01 DOI:10.18653/v1/W17-4402
Francesco Barbieri, Luis Espinosa Anke, Miguel Ballesteros, Juan Soler, Horacio Saggion
{"title":"Towards the Understanding of Gaming Audiences by Modeling Twitch Emotes","authors":"Francesco Barbieri, Luis Espinosa Anke, Miguel Ballesteros, Juan Soler, Horacio Saggion","doi":"10.18653/v1/W17-4402","DOIUrl":null,"url":null,"abstract":"Videogame streaming platforms have become a paramount example of noisy user-generated text. These are websites where gaming is broadcasted, and allows interaction with viewers via integrated chatrooms. Probably the best known platform of this kind is Twitch, which has more than 100 million monthly viewers. Despite these numbers, and unlike other platforms featuring short messages (e.g. Twitter), Twitch has not received much attention from the Natural Language Processing community. In this paper we aim at bridging this gap by proposing two important tasks specific to the Twitch platform, namely (1) Emote prediction; and (2) Trolling detection. In our experiments, we evaluate three models: a BOW baseline, a logistic supervised classifiers based on word embeddings, and a bidirectional long short-term memory recurrent neural network (LSTM). Our results show that the LSTM model outperforms the other two models, where explicit features with proven effectiveness for similar tasks were encoded.","PeriodicalId":207795,"journal":{"name":"NUT@EMNLP","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"24","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"NUT@EMNLP","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18653/v1/W17-4402","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 24

Abstract

Videogame streaming platforms have become a paramount example of noisy user-generated text. These are websites where gaming is broadcasted, and allows interaction with viewers via integrated chatrooms. Probably the best known platform of this kind is Twitch, which has more than 100 million monthly viewers. Despite these numbers, and unlike other platforms featuring short messages (e.g. Twitter), Twitch has not received much attention from the Natural Language Processing community. In this paper we aim at bridging this gap by proposing two important tasks specific to the Twitch platform, namely (1) Emote prediction; and (2) Trolling detection. In our experiments, we evaluate three models: a BOW baseline, a logistic supervised classifiers based on word embeddings, and a bidirectional long short-term memory recurrent neural network (LSTM). Our results show that the LSTM model outperforms the other two models, where explicit features with proven effectiveness for similar tasks were encoded.
通过模拟Twitch表情来理解游戏用户
视频游戏流媒体平台已经成为用户生成文本的典型例子。这些网站播放游戏,并允许观众通过集成聊天室进行互动。这类平台中最著名的可能是Twitch,它的月观众超过1亿。尽管有这些数字,但与其他以短消息为特色的平台(如Twitter)不同,Twitch并没有受到自然语言处理社区的太多关注。在本文中,我们旨在通过提出两个特定于Twitch平台的重要任务来弥合这一差距,即(1)表情预测;(2)网络喷子检测。在我们的实验中,我们评估了三种模型:BOW基线,基于词嵌入的逻辑监督分类器和双向长短期记忆递归神经网络(LSTM)。我们的结果表明,LSTM模型优于其他两种模型,其中编码了对类似任务有效的显式特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信