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Enhancing Concept Based Modeling Approach for Blog Classification

机译:基于概念的博客分类建模方法

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Blogs are user generated content discusses on various topics. For the past 10 years, the social web content is growing in a fast pace and research projects are finding ways to channelize these information using text classification techniques. Existing classification technique follows only boolean (or crisp) logic. This paper extends our previous work with a framework where fuzzy clustering is optimized with fuzzy similarity to perform blog classification. The knowledge base-Wikipedia, a widely accepted by the research community was used for our feature selection and classification. Our experimental result proves that proposed framework significantly improves the precision and recall in classifying blogs.
机译:博客是用户生成的内容,涉及各种主题。在过去的十年中,社交网站的内容正在快速增长,研究项目正在寻找使用文本分类技术来传播这些信息的方法。现有的分类技术仅遵循布尔(或清晰)逻辑。本文将我们以前的工作扩展到一个框架,其中使用模糊相似性对模糊聚类进行优化以执行博客分类。研究社区广泛接受的知识库Wikipedia用于我们的特征选择和分类。我们的实验结果证明,提出的框架大大提高了博客分类的准确性和召回率。

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