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We analyze the language identification algorithms used to identify the Arabic script Web documents such as Arabic, Jawi, Persian and Urdu using independent component analysis (ICA). We have used a combination of Entropy term weighting scheme and class based feature (CPBF) vectors as feature selection methods for selecting the best features of Arabic script Web documents for Web page language identifications. Then we input the selected features based on the identification of latent semantics of user profiles using singular value decomposition (SVD). The SVD has been used to remove the noises on the documents retrieved before applying the ICA for topic extraction. We assume that the topic on each document is independent from each other. We have used the information retrieval measures that are precision, recall and F in order to evaluate the effectiveness of the proposed algorithm. From the experiments, we have found that the proposed method could leads to good Arabic script language identification results with good separations of Arabic, Persian, and Urdu languages using the ICA.