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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
In this paper, reclassification for the current classification through K-means would be implemented based on the feedback of Web usage mining in order to improve the accuracy of news recommendation and convergence of classification. It could extract most relative keywords and eliminate the disturbance of multi-vocal
quality of text-mined data while efficacy relied on the context of the choice of techniques. Although developments of automated keyword extraction methods have made differences in the quality of data selection, the efficacy of the Natural Language Processing (NLP) methods using verified keywords remain a challenge. In this
accuracy than individual classifiers. The maximum accuracy was got by enhancing the ensemble with an additional automatically generated domain specific class wise keyword list. Use of this system gave us greater than 4 percent improvement over the techniques of just using the ensemble classifier. A further improvement in
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
This paper surveys Audio Information Retrieval (AIR) using a literature review and classification of articles from 1994 to 2010 with a keyword index and article abstract in order to explore how AIR methodologies and applications have developed during this period. Based on the scope of many papers and journals of AIR
special data record and new record model on fuzzy set are given. By calculating the membership of keyword, new fuzzy closeness functions are proposed to classify the information. Finally, examples prove that this algorithm can effectively and automatically classify input information of database, the accuracy and intelligence
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
Current classification techniques use word matching and clustering techniques to classify webpages. These techniques use ad hoc approach of checking and matching the entire keywords in a webpage for classification. These methods are efficient but not without problems. In general, they suffer from the following
the websites into their most appropriate category. Several parameters like the weight applied to each feature and the keywords used to classify the websites were tuned to yield better results. The experimental evaluation revealed that the method implemented provides very high accuracy. In particularly, we obtained an
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
Traditional automatic classifiers often conduct misclassifications. Folksonomy, a new manual classification scheme based on tagging efforts of users with freely chosen keywords can effective resolve this problem. Even though the scalability of folksonomy is much higher than the other manual classification schemes, the
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