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The feature extraction is the most key technology of text categorization. The word is used as the feature in the traditional text classification, and its effect for the text classification is evidence. The feature extraction method using base phrase and keyword changes the feature extraction of Chinese text from
classification researches on Vietnamese still are limited. By using a Vietnamese news corpus, we propose some methods to solve Vietnamese news classification problems. By employing the Bag of Words (BoW) with keywords extraction and Neural Network approaches, we trained a machine learning model that could achieve an average of
Twitter, as a social media is a very popular way of expressing opinions and interacting with other people in the online world. When taken in aggregation tweets can provide a reflection of public sentiment towards events. In this paper, we provide a positive or negative sentiment on Twitter posts using a well-known machine learning method for text categorization. In addition, we use manually labeled...
This paper discusses a approach of Chinese text classification on semantic Web. It is given one classified technology based on the semantic concept established on the "How-net" . It extracts keywords from text, analyses the full text using the keywords concept, and then the integrates to classify by categories of
word segmentation and pas tagging, language modeling and term translation, text clustering, text categorization, text summarization, keywords identification in a single document and duplication detection. The application can invoke any module of LJParser in Windows and Linux using any language including C, C# and Java
to describe a document instead of traditional keywords vector, which is based on merging words with high similarity and using a concept to describe the semantic feature rather than a series of words. It not only reduces feature dimension but also adds semantic information to the vector. We also use sentence (document
index texts. Traditional BOW matrix is replaced by ldquoBag of Conceptsrdquo (BOC). For this purpose, we developed fully automated methods for mapping keywords to their corresponding ontology concepts. Support vector machine a successful machine learning technique is used for classification. Experimental results shows that
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