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Due to the exponential growth of available text documents in digital form, it is of great importance to develop techniques for automatic document classification based on the textual contents. Earlier document classification techniques have used keyword-based features and related statistics to achieve good results when
Sentiment analysis in text mining is known to be a challenging task. Sentiment is subtly reflected by the tone, affective state or emotion of a writer's expression in words. Conventional text mining techniques which are based on keyword frequency counting usually run short of accurately detecting such subjective
events. And a huge resource of text-based emotion can be found from the World Wide Web nowadays. This paper reports a study to investigate the effectiveness of using SVM (Support Vector Machine) on linguistic features considering emotion keywords and negative words, and classify a collection of blog posts sentences tagged
likelihood in the entire training documents where the training and test data are split randomly into k-subsets like 2/3 for training and 1/3 for test data. In addition, it also utilizes two level hierarchy structures for training documents like features from title, keywords and content with the predefined knowledge available
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