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Great challenges arise due to the rapid growth of online data. The widespread use of online social networks (OSN) have enabled the generation of massive amounts of raw data where users post their own material. One interesting example of user generated data is their political views and opinions. The ability to crawl OSN and automatically analyze their political content is of undeniable importance. However, this requires automated methods for posts' tone analysis, sentiment analysis, and emotional affect. The purpose of this paper is to evaluate Arabic news posts affect on readers using a novel approach of aspect-based sentiment analysis (ABSA). There are many tasks typically associated with ABSA such as the extraction and polarity identification of aspect terms and categories. The focus of this work is on the tasks related to aspect terms. A typical approach to address these tasks goes through several stages of text pre-processing, features extraction and classification. This paper follows this approach and makes use of widely used features and classifiers. The features considered include Part of Speech (POS) tagging, Named Entity Recognition (NER), and N-Grams. As for the considered classifiers, they are: Conditional Random Fields (CRF), Decision Tree (J48), Naive Bayes and K-Nearest Neighbor (IBk). A set of experiments are conducted to compare the considered classifiers against each other and against a baseline classifier that is very common for ABSA. The results show that the extracted features allow all of the four considered classifiers to significantly outperform the baseline classifier. They also show that J48 performs the best for the task of aspect terms extraction whereas CRF and Naive Bayes are slightly better in aspect terms polarity identification.

Great challenges arise due to the rapid growth of online data. The widespread use of online social networks (OSN) have enabled the generation of massive amounts of raw data where users post their own material. One interesting example of user generated data is their political views and opinions. The ability to crawl OSN and automatically analyze their political content is of undeniable importance. However, this requires automated methods for posts' tone analysis, sentiment analysis, and emotional affect. The purpose of this paper is to evaluate Arabic news posts affect on readers using a novel approach of aspect-based sentiment analysis (ABSA). There are many tasks typically associated with ABSA such as the extraction and polarity identification of aspect terms and categories. The focus of this work is on the tasks related to aspect terms. A typical approach to address these tasks goes through several stages of text pre-processing, features extraction and classification. This paper follows this approach and makes use of widely used features and classifiers. The features considered include Part of Speech (POS) tagging, Named Entity Recognition (NER), and N-Grams. As for the considered classifiers, they are: Conditional Random Fields (CRF), Decision Tree (J48), Naive Bayes and K-Nearest Neighbor (IBk). A set of experiments are conducted to compare the considered classifiers against each other and against a baseline classifier that is very common for ABSA. The results show that the extracted features allow all of the four considered classifiers to significantly outperform the baseline classifier. They also show that J48 performs the best for the task of aspect terms extraction whereas CRF and Naive Bayes are slightly better in aspect terms polarity identification.

AbstractSperm mitochondrial dysfunction causes the generation of an insufficient amount of energy needed for sperm motility. This will affect sperm fertilization capacity, and thus, most asthenozoospermic men usually require assisted reproductive techniques. The etiology of asthenozoospermia remains largely unknown. The current study aimed to investigate the effect of mitochondrial genetic variants on sperm motility and intracytoplasmic sperm injection (ICSI) outcomes. A total of 150 couples from the ICSI cycle were enrolled in this study. One hundred five of the male partners were asthenozoospermic patients, and they were subdivided into three groups according to their percentage of sperm motility, while forty-five of the male partners were normozoospermic. Genetic variants were screened using direct Sanger's sequencing in four mitochondrial genes (nicotinamide adenine dinucleotide hydrogen (NADH) dehydrogenase 1 (ND1), NADH dehydrogenase 2 (ND2), NADH dehydrogenase 5 (ND5), and NADH dehydrogenase 6 (ND6)). We identified three significant variants: 13708G>A (rs28359178) in ND5, 4216T>C (rs1599988) in ND1, and a novel 12506T>A in ND5 with P values 0.006, 0.036, and 0.013, respectively. The medians of sperm motility, fertilization rate, embryo cleavage score, and embryo quality score were significantly different between men showing 4216T>C, 12506T>A, 13708G>A and wild type, Mann-Whitney P values for the differences in the medians were < 0.05 in all of them. The results from this study suggest that 13708G>A, 12506T>A, and 4216 T>C variants in sperm mitochondrial DNA negatively affect sperm motility and ICSI outcomes.