通过音乐流媒体平台上的歌曲评论增强音乐聆听体验
Amplifying the Music Listening Experience through Song Comments on Music Streaming Platforms
AI总结：
本研究利用深度学习从歌曲评论中提取关键词、情感与诱发机制，并通过标签展示和地图隐喻重组评论，以提升年轻用户在音乐流媒体平台上的个性化情感体验。
摘要：
音乐流媒体服务在年轻一代中日益流行，他们通过在评论中表达个人感受和分享主观情感来寻求社交体验。然而，当前平台往往忽视这些情感方面，影响了听众找到能够触发特定个人感受的音乐的能力。为弥补这一不足，本研究提出一种新颖的方法，利用深度学习方法从歌曲评论中捕捉上下文关键词、情感和诱发机制。本研究为现有音乐应用增加了两个功能，包括展示最能代表歌曲评论的标签，以及一种新颖的地图隐喻，根据时间顺序、内容和情感重新组织歌曲评论。通过一个使用场景和一项用户研究验证了所提方法的有效性，证明了其能够改善用户探索歌曲和浏览感兴趣评论的体验。本研究通过为年轻一代提供更加个性化和情感丰富的音乐体验，推动了音乐流媒体服务的发展。
英文摘要：
Music streaming services are increasingly popular among younger generations who seek social experiences through personal expression and sharing of subjective feelings in comments. However, such emotional aspects are often ignored by current platforms, which affects the listeners' ability to find music that triggers specific personal feelings. To address this gap, this study proposes a novel approach that leverages deep learning methods to capture contextual keywords, sentiments, and induced mechanisms from song comments. The study augments a current music app with two features, including the presentation of tags that best represent song comments and a novel map metaphor that reorganizes song comments based on chronological order, content, and sentiment. The effectiveness of the proposed approach is validated through a usage scenario and a user study that demonstrate its capability to improve the user experience of exploring songs and browsing comments of interest. This study contributes to the advancement of music streaming services by providing a more personalized and emotionally rich music experience for younger generations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
