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In this paper, we present a new algorithm based on the LDA (Latent Dirichlet Allocation) and the Support Vector Machine (SVM) used in the classification of Arabic texts. Current research usually adopts Vector Space Model to represent documents in Text Classification applications. In this way, document is coded as a vector of words; n-grams. These features(More)
Our researches works are interested on the application of the intertextual distance theory on the Arabic language as a tool for the classification of texts. This theory assumes the classification of texts according to criteria of lexical statistics, and it is based on the lexical connection approach. Our objective is to integrate this theory as a tool of(More)
In this paper, the authors present latent topic model to index and represent the Arabic text documents reflecting more semantics. Text representation in a language with high inflectional morphology such as Arabic is not a trivial task and requires some special treatments. The authors describe our approach for analyzing and preprocessing Arabic text then we(More)
Today, the need to automatically process opinions is strongly felt. It is in this context that we situate this work whose objective is to contribute to the achievement of opinions analysis system, enabling a binary classification on a set of textual data. For this, we studied and evaluated several methods, Support Vector Machines (SVM) and Naïve Bayes (NB),(More)
In this paper we have presented a brief current state of the Art for Arabic text representation and classification methods. First we describe some algorithms applied to classification on Arabic text. Secondly, we cite all major works when comparing classification algorithms applied on Arabic text, after this, we mention some authors who proposing new(More)
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