基于词袋模型的文本分类实现

Nisha V M, D. Kumar R
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引用次数: 2

摘要

词袋提供了一种处理文本表示的方法,并将其应用于标准类型的文本排列。这种方法依赖于词汇袋(BOW)的概念,它测量从维基百科、Kaggle、Gmail等网站上可访问的内容。利用所提出的方法创建一个向量空间模型,该模型真正持续为支持向量机分类器。这是为了整理和收集可通过社交媒体公开访问的数据集的文档记录。文本结果演示了在词云上查看的原始信息和干净信息之间的检查。
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Implementation on Text Classification Using Bag of Words Model
Bag of words provides one way to deal with text representation and apply it to a standard type of text arrangement. This method depends on the idea of Bag-of-Words (BOW) that measures the content which is accessible from Wikipedia, Kaggle, Gmail and so on. The proposed method is utilized to create a Vector Space Model, which truly sustained into a Support Vector Machine classifier. This is to arrange and gathering of document records that are publically accessible datasets through social media. The text results demonstrate the examination between the raw information and the clean information that is viewed on the word cloud.
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