Mood and emotion play an important role when it comes to choosing musical tracks to listen to. In the field of music information retrieval and recommendation, emotion is considered contextual information that is hard to capture, albeit highly influential. In this study, we analyze the connection between users' emotional states and their musical choices. Particularly, we perform a large-scale study based on two data sets containing 560,000 and 90,000 #nowplaying tweets, respectively. We extract affective contextual information from hashtags contained in these tweets by applying an unsupervised sentiment dictionary approach. Subsequently, we utilize a state-of-the-art network embedding method to learn latent feature representations of users, tracks and hashtags. Based on both the affective information and the latent features, a set of eight ranking methods is proposed. We find that relying on a ranking approach that incorporates the latent representations of users and tracks allows for capturing a user's general musical preferences well (regardless of used hashtags or affective information). However, for capturing context-specific preferences (a more complex and personal ranking task), we find that ranking strategies that rely on affective information and that leverage hashtags as context information outperform the other ranking strategies.

中文翻译：

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利用情感标签对音乐推荐进行排名

在选择要听的音乐曲目时，情绪和情感起着重要作用。在音乐信息检索和推荐领域，情感被认为是难以捕捉的上下文信息，尽管影响很大。在这项研究中，我们分析了用户的情绪状态与其音乐选择之间的联系。特别是，我们基于分别包含 560,000 和 90,000 #nowplaying 推文的两个数据集进行了大规模研究。我们通过应用无监督的情感词典方法从这些推文中包含的主题标签中提取情感上下文信息。随后，我们利用最先进的网络嵌入方法来学习用户、曲目和主题标签的潜在特征表示。基于情感信息和潜在特征，提出了一套八种排名方法。我们发现依靠结合用户和曲目的潜在表示的排名方法可以很好地捕捉用户的一般音乐偏好（无论使用的主题标签或情感信息如何）。然而，为了捕获特定于上下文的偏好（更复杂和个人的排名任务），我们发现依赖情感信息并利用主题标签作为上下文信息的排名策略优于其他排名策略。