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    政大機構典藏 > 資訊學院 > 資訊科學系 > 期刊論文 >  Item 140.119/142229


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    題名: Leveraging Affective Hashtags for Ranking Music Recommendations
    作者: 蔡銘峰
    Tsai, Ming-Feng
    Zangerle, Eva;Chen, Chih-Ming;Yang, Yi-Hsuan
    貢獻者: 資科系
    關鍵詞: Emotion in music;emotion regulation;sentiment detection;ranking;music recommendation;microblogging;hashtags
    日期: 2018-06
    上傳時間: 2022-10-07
    摘要: 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.
    關聯: IEEE Transactions on Affective Computing, 12(1), 78-91
    資料類型: article
    DOI 連結: https://doi.org/10.1109/TAFFC.2018.2846596
    DOI: 10.1109/TAFFC.2018.2846596
    顯示於類別:[資訊科學系] 期刊論文

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