Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
The Journal of Machine Learning Research
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The text classification methods heavily depend on machine learning algorithms with abstract mathematic metrics, which obstruct the direct observation and intuitive understanding of the text-specific classification. In this paper, we model a document as a Document-Classes-Topics top-down hierarchical structure. Furthermore, by running the document generation procedure, we can obtain each class's content share, which not only can be used to make the classification decision but also can provide a natural visualization approach for text classification. We implement this idea by a new tool named TC-DCA, which provides the visualization of text classification result, where the target document is expressed graphically as its content's allocation on every class. TC-DCA can also perform the drilling down operation to reveal the classification effect of each word of the document.