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Along with the development of social network, more and more people know the world by reading news. The problem about what kind of emotion is inspired when people read news is very worthy of discussion. This paper will mix Deep Belief Networks (DBN) model and Support Vector Machine (SVM) to a hybrid neural network model by using the Contrast Divergence (CD) algorithm to estimate the weights when training...
In this paper, Deep Belief Nets(DBN) model and Support Vector Machine(SVM) are used to mine the features hidden in social news, which can influence the emotions of men. Three feature selection methods for text modeling are used to build the input vectors of DBN, with the purpose of keeping the text information to the greatest extent. We take advantage of the deep features abstracted by DBN to build...
To enhance the automatic text classification task, this paper proposes a novel approach for treating the problem of inductive bias incurred by the centroid classifier assumption. This approach is a trainable classifier, which takes into account tfidf as a text feature. The main goal of the proposed approach is to take advantage of the most similar training errors in the classification model for successively...
As a result of advance in technology, there now exist a large amount of online documents in the form of surveys or called reviews. Most of the previous work on text classification is focusing on sentiment text classification. Sentiment classification requires the knowledge data of vocabulariespsila semantic meaning and the relationships between the vocabularies. In this paper, sentiment features of...
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