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Predicting the severity of bugs has been found in past research to improve triaging and the bug resolution process. For this reason, many classification/prediction approaches emerged over the years to provide an automated reasoning over severity classes. In this paper, we use text mining together with bi-grams and feature selection to improve the classification of bugs in severe/non-severe classes...
Songs feel emotionally different to listeners depending on their lyrical contents, even when melodies are similar. Accordingly, when using features related to melody, like tempo, rhythm, tune, and musical note, it is difficult to classify emotions accurately through the existing music emotion classification methods. This paper therefore proposes a method for lyrics-based emotion classification using...
Document categorization is an important topic that is central to many applications that demand reasoning about and organisation of text documents, web pages, and so forth. Document classification is commonly achieved by choosing appropriate features (terms) and building a term-frequency inerse-document frequency (TFIDF) feature vector. In this process, feature selection is a key factor in the accuracy...
This paper presents a method for forecasting the change of intraday stock price by utilizing text mining news of stock. This method is based on text mining techniques coupled with rough sets theories and support vector machine classifier. The method can handle without difficulty unstructured news of Taiwan stock market through preprocessing, feature selection and mark. The method also extracts the...
This work proposes a hybrid model for text document classification for information retrieval using Naive Bayes and Rough set theory. Rough set theory is used for feature reduction and Naive Bayes theorem is used for classification of documents into the predefined categories by means of the probabilistic values. The deployment of the proposed model is planned through an enhanced method of the utilization...
This paper presents the results of classifying Arabic text documents using a decision tree algorithm. Experiments are performed over two self collected data corpus and the results show that the suggested hybrid approach of Document Frequency Thresholding using an embedded information gain criterion of the decision tree algorithm is the preferable feature selection criterion. The study concluded that...
Text categorization is a key issue of text mining. Although there are many studies on this problem, the majority of them are focused on classification of rough categories. In this kind of problem, there are obviously different features that can differentiate one category from others. Only very few researches concerned fine text categorization (FTC) problem which is characterized by many duplicated...
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