01899nas a2200145 4500008004100000245010300041210006900144260001000213300001000223490000700233520142700240100001801667700002101685856004701706 2021 eng d00aEfficient Measuring of Readability to Improve Documents Accessibility for Arabic Language Learners0 aEfficient Measuring of Readability to Improve Documents Accessib c09/21 a75-820 v193 a

This paper presents an approach based on supervised machine learning methods to build a classifier that can identify text complexity in order to present Arabic language learners with texts suitable to their levels. The approach is based on machine learning classification methods to discriminate between the different levels of difficulty in reading and understanding a text. Several models were trained on a large corpus mined from online Arabic websites and manually annotated. The model uses both Count and TF-IDF representations and applies five machine learning algorithms; Multinomial Naïve Bayes, Bernoulli Naïve Bayes, Logistic Regression, Support Vector Machine and Random Forest, using unigrams and bigrams features. With the goal of extracting the text complexity, the problem is usually addressed by formulating the ‘level identification’ as a classification task. Experimental results showed that n-gram features could be indicative of the reading level of a text and could substantially improve performance, and showed that SVM and Multinomial Naïve Bayes are the most accurate in predicting the complexity level. Best results were achieved using TF-IDF Vectors trained by a combination of word-based unigrams and bigrams with an overall accuracy of 87.14% over four classes of complexity.

1 aBessou, Sadik1 aChenni, Ghozlane uhttp://www.dline.info/download.php?sn=332601432nas a2200145 4500008004100000245006400041210006300105260001200168300001200180490000700192520099300199100001801192700002001210856005601230 2019 eng d00aSubjective Sentiment Analysis for Arabic Newswire Comments-0 aSubjective Sentiment Analysis for Arabic Newswire Comments c10/2019 a289-2950 v173 a

This paper presents an approach based on supervised machine learning methods to discriminate between positive, negative and neutral Arabic reviews in online newswire. The corpus is labeled for subjectivity and sentiment analysis (SSA) at the sentence-level. The model uses both count and TF-IDF representations and apply six machine learning algorithms; Multinomial Naïve Bayes, Support Vector Machines (SVM), Random Forest, Logistic Regression, Multi-layer perceptron and k-nearest neighbors using uni-grams, bi-grams features. With the goal of extracting users’ sentiment from written text. Experimental results showed that n-gram features could substantially improve performance; and showed that the Multinomial Naïve Bayes approach is the most accurate in predicting topic polarity. Best results were achieved using count vectors trained by combination of word-based uni-gramsand bi-grams with an overall accuracy of 85.57% over two classes and 65.64% over three classes.

1 aBessou, Sadik1 aAberkane, Rania uhttp://dline.info/fpaper/jdim/v17i5/jdimv17i5_3.pdf