{"title":"使用 TF-IDF 和支持向量机对 \"玄心对 X 的影响:心理健康的意义 \"进行情感分析","authors":"Sava Irhab Atma Jaya, Junta Zeniarja","doi":"10.33395/sinkron.v8i3.13716","DOIUrl":null,"url":null,"abstract":"Genshin Impact are now an integral part of daily life for many, potentially influencing mental well-being. Sentiment analysis window into these emotional effects, especially given the varied findings on gaming's impact on mental health. Analyzing X responses Genshin Impact using Support Vector Machine crucial, given its effectiveness in sentiment analysis. This study aims to deepen our understanding game's psychological impact and support development mental health interventions for gamers. The SVM classification report shows promising precision: 0.68 for Negative, 0.63 for Neutral, and 0.72 for Positive sentiment. However, recall rates favor Positive reviews (0.87) over Negative (0.56) and Neutral (0.51), reflected in the F1 score, highest for Positive sentiment at 0.79. With 174 Negative, 216 Neutral, and 333 Positive support counts, model achieved an overall accuracy of 0.69, effectively classifying Genshin Impact reviews based on sentiment. Analysis findings suggest a prevalence of positive opinions, indicating widespread player satisfaction with the game.","PeriodicalId":34046,"journal":{"name":"Sinkron","volume":"65 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Sentiment Analysis of Genshin Impact on X: Mental Health Implications Using TF-IDF and Support Vector Machine\",\"authors\":\"Sava Irhab Atma Jaya, Junta Zeniarja\",\"doi\":\"10.33395/sinkron.v8i3.13716\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Genshin Impact are now an integral part of daily life for many, potentially influencing mental well-being. Sentiment analysis window into these emotional effects, especially given the varied findings on gaming's impact on mental health. Analyzing X responses Genshin Impact using Support Vector Machine crucial, given its effectiveness in sentiment analysis. This study aims to deepen our understanding game's psychological impact and support development mental health interventions for gamers. The SVM classification report shows promising precision: 0.68 for Negative, 0.63 for Neutral, and 0.72 for Positive sentiment. However, recall rates favor Positive reviews (0.87) over Negative (0.56) and Neutral (0.51), reflected in the F1 score, highest for Positive sentiment at 0.79. With 174 Negative, 216 Neutral, and 333 Positive support counts, model achieved an overall accuracy of 0.69, effectively classifying Genshin Impact reviews based on sentiment. Analysis findings suggest a prevalence of positive opinions, indicating widespread player satisfaction with the game.\",\"PeriodicalId\":34046,\"journal\":{\"name\":\"Sinkron\",\"volume\":\"65 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sinkron\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.33395/sinkron.v8i3.13716\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sinkron","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.33395/sinkron.v8i3.13716","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
现在,"元心冲击 "已成为许多人日常生活中不可或缺的一部分,可能会对心理健康产生影响。情感分析是了解这些情感影响的窗口,特别是考虑到游戏对心理健康影响的不同研究结果。鉴于支持向量机在情感分析中的有效性,使用支持向量机分析 X 回应 "玄心影响 "至关重要。这项研究旨在加深我们对游戏的心理影响的理解,并为开发游戏玩家心理健康干预措施提供支持。SVM 分类报告显示了良好的精确度:负面情绪为 0.68,中性情绪为 0.63,正面情绪为 0.72。不过,召回率方面,正面评论(0.87)优于负面评论(0.56)和中性评论(0.51),这反映在 F1 分数上,正面情感的 F1 分数最高,为 0.79。通过 174 个负面、216 个中性和 333 个正面支持计数,模型的总体准确率达到了 0.69,有效地根据情感对源信 Impact 评论进行了分类。分析结果表明,正面意见普遍存在,表明玩家对游戏的满意度普遍较高。
Sentiment Analysis of Genshin Impact on X: Mental Health Implications Using TF-IDF and Support Vector Machine
Genshin Impact are now an integral part of daily life for many, potentially influencing mental well-being. Sentiment analysis window into these emotional effects, especially given the varied findings on gaming's impact on mental health. Analyzing X responses Genshin Impact using Support Vector Machine crucial, given its effectiveness in sentiment analysis. This study aims to deepen our understanding game's psychological impact and support development mental health interventions for gamers. The SVM classification report shows promising precision: 0.68 for Negative, 0.63 for Neutral, and 0.72 for Positive sentiment. However, recall rates favor Positive reviews (0.87) over Negative (0.56) and Neutral (0.51), reflected in the F1 score, highest for Positive sentiment at 0.79. With 174 Negative, 216 Neutral, and 333 Positive support counts, model achieved an overall accuracy of 0.69, effectively classifying Genshin Impact reviews based on sentiment. Analysis findings suggest a prevalence of positive opinions, indicating widespread player satisfaction with the game.