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Text classification is one of the key methods used in text mining. Generally, traditional classification algorithms from machine learning field are used in text classification. These algorithms are primarily designed for structured data. In this paper, we propose a new classifier for textual data, called Supervised Meaning Classifier (SMC). The new SMC classifier uses meaning measure, which is based...
In this paper, we investigate the use of Text Classification techniques to extract contextual information from user reviews for Context Aware Recommendation. We conduct several experiments to identify the best Text Representation settings and the best classification algorithm for our dataset. We carry out our experiments on hotel reviews. We focus on extracting the trip type, as contextual information,...
In usual Information Retrieval (IR) systems, the user query is represented in the form of a keyword set. Information resources are retrieved according to their similarities to this query. Consequently if query is not declared with appropriate terms, retrieved results would not be satisfactory. Therefore query refinement procedures are incorporated to improve the efficiency of the IR systems.
In order to overcome the SVM for text classification ignoring the context of semantic information and the use of a community to text classification, one boundary point can only belong to a community of view, the concept of contribution and overlapping coefficient based on the complex network diagram is introduced. And feature selection algorithm based on community discovery is proposed. Experiments...
Document classification is critical due to explosive increasing of text in modern world. However, most of existing document classification algorithms are easily affected by noise data. Therefore, in document classification tasks, the ability of noise control is as important as the ability to classify exactly. In this paper, we propose a novel classification framework based on fuzzy formal concept...
The continuing explosive growth of textual content within the World Wide Web has given rise to the need for sophisticated Text Classification (TC) techniques that combine efficiency with high quality of results. E-mail filtering is one application that has the potential to affect every user of the internet. Even though a large body of research has delved into this problem, there is a paucity of survey...
Current classification methods are based on the ldquobag of wordsrdquo (BOW) representation, which only accounts for term frequency in the documents, and ignores important semantic relationships between key terms. In this paper, we proposed a system that uses ontologies and natural language processing techniques to index texts. Traditional BOW matrix is replaced by ldquoBag of Conceptsrdquo (BOC)...
Successful text classification is highly dependent on the representations used. Currently, most approaches to text classification adopt the ‘bag-of-words’ document representation approach, where the frequency of occurrence of each word is considered as the most important feature, but this method ignores important semantic relationships between key terms. In this paper, we proposed a system that uses...
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