S14全球总决赛承载着LPL粉丝的情感寄托,文章旨在探索观众在比赛期间的情绪反应和社交互动。以2024年英雄联盟全球总决赛的弹幕评论数据为例,开展了词频分析、词云图可视化以及基于LDA模型的主题解析,并利用SnowNLP进行了情感语义分析。研究表明,粉丝关注比赛结果及游戏角色,并深入讨论战术和表现相关话题。根据 LDA 主题分析,结合困惑度计算与 pyLDAvis可视化显示,最佳主题数量为4个,并得出竞赛相关的4个主题。此外,通过情感分析占比图和直方图呈现出弹幕背后蕴含的情感互动,2024年英雄联盟全球总决赛虽然以全华班BLG败北收尾,导致粉丝们情绪较为低迷,但整体情感倾向仍较为积极。
Abstract
The S14 Global Finals serve as a significant cultural touchstone and emotional anchor for LPL fans. This article aims to delve into and examine the emotional responses and social interactions of audiences during the tournament. Take the bullet comments data of the 2024 League of Legends Global Finals for example, this article conducts word frequency analysis, word cloud visualization, and topic analysis based on LDA model, and uses SnowNLP for emotional semantic analysis. Research shows that fans pay attention to game results and game characters, and engage in in-depth discussions on tactics and performance related topics. Based on LDA theme analysis, combined with perplexity calculation and pyLDAvis visualization display, the optimal number of themes is 4, and 4 themes related to the competition are identified. In addition, the emotion analysis ratio chart and histogram show the emotional interaction behind the bullet screen. Although the 2024 League of Legends Global Finals ended with the defeat of the all-Chinese class BLG, the fans' emotions were relatively depressed, but the overall emotional tendency was still relatively positive.
关键词
英雄联盟 /
弹幕评论 /
主题分析 /
词频分析 /
情感分析
Key words
League of Legends /
barrage commentary /
thematic analysis /
word frequency analysis /
sentiment analysis
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] NICOLE.从收视率出发,英雄联盟离不开Faker[J].电子竞技,2023(12):56-59.
[2] 杜利明,郭文艳,崔蕾,等.基于LDA的电商平台用户评论挖掘与情感分析研究——以京东商城App为例[J].江苏科技信息,2024,41(12):125-129.
[3] 罗向东,强威,张希莹,等.基于文本挖掘的跑鞋用户评价及情感分析[J].丝绸,2024,61(6):108-119.
[4] 张平霞. 基于文本挖掘的MOOC讨论区学习评价研究[D].重庆:重庆师范大学,2018.
[5] 李丹丹. 基于在线课程评论的情感识别模型构建及模型评价研究[D].贵阳:贵州师范大学,2024.
[6] 盛蒙蒙,马溯,顾孟钧,等.基于SVM和LSTM的在线剧评分析模型[J].现代计算机,2024,30(2):60-65.
[7] 蓝钰. 基于Python技术的电影《白蛇传·情》豆瓣短评文本挖掘与可视化分析[J].文化创新比较研究,2024,8(10):52-56.
[8] 邹墨馨,辛雨璇.基于文本挖掘的影视弹幕情感分析研究[J].科技创新与应用,2021,11(24):51-53.
[9] 王智迪. 基于LDA与snowNLP的学术造假事件主题挖掘与情感分析[J].软件导刊,2025,24(3):92-98.
[10] Nastiti K R,Hidayatullah A F,Pratama A R.Discovering computer science research topic trends using latent dirichlet allocation[J].Jurnal Online Informatika,2021,6(1):17-24.
基金
教育厅本科教改课题,项目编号:2024JGA398; 广西高校中青年教师科研基础能力提升项目,项目编号:2024KY0904