Amplifying the Music Listening Experience through Song Comments on Music Streaming Platforms

TL;DR

This paper introduces a deep learning-based approach to enhance music streaming platforms by incorporating emotional and contextual insights from song comments, thereby enriching user experience and personalization.

Contribution

It presents a novel method for analyzing song comments to improve music exploration through tags and a map metaphor, enhancing emotional engagement.

Findings

Improved user experience in song exploration

Effective extraction of emotional and contextual comment features

Enhanced personalization in music streaming platforms

Abstract

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.