用户内容分类模型,一个结合了文本挖掘和语义模型的通用模型

Randa Benkhelifa, Ismaïl Biskri, F. Z. Laallam, Esma Aïmeur
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引用次数: 3

摘要

社交网站不仅在用户数量上增长,而且在用户生成内容方面也在增长。这些数据代表了几个应用程序的有价值的信息源,这些应用程序需要与个人数据关联的内容的含义。然而,目前的社交网络结构不允许以一种快速直接的方式提取这些应用程序所寻求的隐藏信息。语义web社区已经做出了很大的努力来解决这个问题,试图尽可能准确地表示用户。他们并不是不能理解用户生成的内容。为此,需要在内容上做更多有意义的工作,以丰富用户配置文件。本文介绍了一种通用的用户内容分类模型(UCC)。它将文本挖掘方法结合到语义模型中,通过包含用户帖子分类信息来丰富用户配置文件。
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User content categorisation model, a generic model that combines text mining and semantic models
Social networking websites are growing not only regarding the number of users but also in terms of the user-generated content. These data represent a valuable source of information for several applications, which require the meaning of that content associated with the personal data. However, the current structure of social networks does not allow extracting in a fast and straightforward way the hidden information sought by these applications. Major efforts have emerged from the semantic web community addressing this problem trying to represent the user as accurately as possible. They are not unable to give a sense to the user-generated content. For this, more sense-making needs to be done on the content, to enrich the user profile. In this paper, we introduce a generic model called user content categorisation (UCC). It incorporates the text mining approach into a semantic model to enrich the user profile by including information on user's posts classifications.
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