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Web news articles play an important role in stock market. Sentiment classification of news articles can help the investors make investment decisions more efficiently. In this paper, we implemented an approach of Chinese new words detection by using N-gram model and applied the result for Chinese word segmentation and sentiment classification. Appraisal theory was introduced into sentiment analysis...
Testing web services for robustness is an effective way of disclosing software bugs. However, when executing robustness tests, a very large amount of service responses has to be manually classified to distinguish regular responses from responses that indicate robustness problems. Besides requiring a large amount of time and effort, this complex classification process can easily lead to errors resulting...
This paper put forward a text categorization method based on Naive Bayes learning support vector machine. First adopt the text pre-processing. Then vector space model and linked list of technical are used to extract text features, reduce dimensions according to the characteristics of the text. Then after Naive Bayes algorithm been proposed to train the support vector machines, support vector machines...
Text categorization is an important research direction of current information retrieval. The traditional text classification method use the support vector machine (SVM) and the Bayes classification algorithm (etc). On the basis of Rough Set on text categorization, this paper put forward the idea of variable precision rough set model for Chinese text categorization, which use the attribute reduct algorithm...
Text classification plays an important role in information extraction and summarization, text retrieval, and question-answering. The discriminative multinomial naive Bayes classifier has been a focus of research in the field of text classification. This paper increases the accuracy of discriminative multinomial Bayesian classifier with the usage of the feature selection technique that evaluates the...
In this paper, a general decision layer classification fusion model, based on information fusion for improving classification precision, is proposed, that is, different multi-classification algorithms as the feature layer doing respective classification, and the results of classification algorithms are input into decision level, the last classification result is output.This model is applied into improving...
This paper performs a comparative analysis of several different types of SMS text classifiers: weight enhanced Multinomial naive Bayes, Poisson naive Bayes, and L2-loss Support Vector Machine. The effects of preprocessing and incorporating additional features on the classifiers were examined. The preliminary experimental results show that the use of preprocessing and incorporating additional features...
How to use the incremental training corpus to improve the question classification accuracy rate in the process of question classification based on statistic learning. A question classification method based on the incremental modified Bayes was presented in this paper. The method used the modified Bayes and combined the incremental learning to correct the parameter by the incremental training set stage...
In the text literature, many Bayesian generative models were proposed to represent documents and words in order to process text effectively and accurately. As the most popular one of these models, Latent Dirichlet Allocation Model(LDA) did great job in dimensionality reduction for document classification. In this paper, inspiring by latent Dirichlet allocation model, we propose LDCM or latent Dirichlet...
This study is made for investigating the performance of artificial immune recognition systems on genre and author detection by using method referenced as (H. Yildiz et al., 2007) based on representation of a document in a different scheme. Most of the studies done nowadays depend on bag of words model which takes the roots or the stems of the words as features. This situation both increases the number...
This work implements an enhanced hybrid classification method through the utilization of the naive Bayes approach and the support vector machine (SVM). In this project, the Bayes formula was used to vectorize (as opposed to classify) a document according to a probability distribution reflecting the probable categories that the document may belong to. The Bayes formula gives a range of probabilities...
In this paper an analysis is given of the application of Bayesian Gaussian process statistical learning algorithms to the problem of text categorization. It is demonstrated that the informative vector machine method, as a sparse Bayesian compression scheme, provides results better than those obtained so far with the support vector machine method, with much less computational cost
In this paper an analysis is given of the application of Bayesian Gaussian process statistical learning algorithms to the problem of text categorization. It is demonstrated that the informative vector machine method, as a sparse Bayesian compression scheme, provides results better than those obtained so far with the support vector machine method, with much less computational cost
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