How do you feel about "dancing queen"?: deriving mood & theme annotations from user tags
How do you feel about "dancing queen"?: deriving mood & theme annotations from user tags
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你对“舞后”有何感想？：衍生情绪
DOI：
10.1145/1555400.1555448
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
Raluca Paiu
中科院分区：
文献类型：
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作者：
Kerstin Bischoff;C. S. Firan;W. Nejdl;Raluca Paiu
Web 2.0支持用户之间的信息共享和协作，最重要的是支持用户的积极参与和创造力。因此，现在有大量手动创建的描述各种资源的元数据可用。这种语义丰富的用户生成的注释是特别有价值的数字图书馆，涵盖多媒体资源，如音乐，这些元数据使检索不仅依赖于内容为基础的（低级别）的功能，但也对标签表示的文本描述。然而，如果我们分析用户为音乐曲目生成的注释，我们会发现它们严重偏向于流派。以前的工作调查类型的用户提供的注释的音乐曲目表明，类型的标签，这将是真正有益的支持检索-使用（主题）和意见（情绪）标签-往往被忽视的用户在注释过程中。在本文中，我们解决这个问题：为了支持用户在标记和填补这些空白的标签空间，我们开发的算法推荐情绪和主题注释。我们的方法利用可用的用户注释，音乐曲目的歌词，以及两者的组合。我们还将我们推荐的情绪/主题注释的结果与体裁和风格推荐进行比较-这是一个更容易和已经研究过的任务。除了与专家（AllMusic.com）进行实地评估外，我们还通过基于Facebook的用户研究来评估我们推荐的标签的质量。我们的研究结果是非常有前途的专家以及用户相比，并提供有趣的见解音乐标签系统，以支持音乐搜索的可能扩展。
Web 2.0 enables information sharing, collaboration among users and most notably supports active participation and creativity of the users. As a result, a huge amount of manually created metadata describing all kinds of resources is now available. Such semantically rich user generated annotations are especially valuable for digital libraries covering multimedia resources such as music, where these metadata enable retrieval relying not only on content-based (low level) features, but also on the textual descriptions represented by tags. However, if we analyze the annotations users generate for music tracks, we find them heavily biased towards genre. Previous work investigating the types of user provided annotations for music tracks showed that the types of tags which would be really beneficial for supporting retrieval - usage (theme) and opinion (mood) tags - are often neglected by users in the annotation rocess. In this paper we address exactly this problem: in order to support users in tagging and to fill these gaps in the tag space, we develop algorithms for recommending mood and theme annotations. Our methods exploit the available user annotations, the lyrics of music tracks, as well as combinations of both. We also compare the results for our recommended mood / theme annotations against genre and style recommendations - a much easier and already studied task. Besides evaluating against an expert (AllMusic.com) ground truth, we evaluate the quality of our recommended tags through a Facebook-based user study. Our results are very promising both in comparison to experts as well as users and provide interesting insights into possible extensions for music tagging systems to support music search.