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.

翻译：音乐流媒体服务在年轻一代中日益流行，他们通过评论中的个人表达和主观感受分享寻求社交体验。然而，这些情感方面往往被当前平台所忽视，影响了听众找到能触发特定个人感受的音乐。为弥补这一不足，本研究提出一种新方法，利用深度学习技术从歌曲评论中捕捉上下文关键词、情感和诱发机制。本研究在现有音乐应用程序中增强了两项功能，包括呈现最能代表歌曲评论的标签，以及一种基于时间顺序、内容和情感重组歌曲评论的新型地图隐喻。通过使用场景和用户研究验证了所提方法的有效性，证明其能够提升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个性化且富有情感的音乐体验，推动了音乐流媒体服务的发展。