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The main objective of the present study is to explore whether there are variations in the employment of evaluative language resources by male and female writers. More specifically, the study focuses on variations, if any, that can be attributed to difference in gender.

The study compared and contrasted forty recommendation letters written by male academics to the same number of letters written by female recommenders. The study uses both quantitative and qualitative approaches.

The investigation of three attitudinal resources in letters of recommendations showed that the most employed resource was the judgment sub-system. The appreciation domain was in the second position, and the least frequent was the affect. The results also revealed no statistically significant variations in attitude sub-systems: Affect and appreciation as the writers in both groups (males and females) employed almost the same options in each. In respect with judgment, however, the analysis explored significant differences between the two sets as male academics used more judgment resources than females.

The main contributions of this study may be as follows: first, it is one of very few studies drawing on the attitude-category of appraisal system, as an analytical tool to examine gender differences in recommendation letters very particularly on the ones written by non-native speakers of English. Second, the gender factor is central in the genre of the recommendation letters and hence researchers should be cognizant of its role as certain variations might be impacted by it. Third, the lists of tokens can be offered as heuristics for academics to have most common words or phrases to use in their letters. Finally, the findings can hopefully bear some important pedagogical implications, very specifically for novice and non-native academic writers of recommendations letters.

This paper aims to propose a novel way of using textual clustering as a feature selection method. It is applied to identify the most important keywords in the profile classification. The method is demonstrated through the problem of sick-leave promoters on Twitter.

Four machine learning classifiers were used on a total of 35,578 tweets posted on Twitter. The data were manually labeled into two categories: promoter and nonpromoter. Classification performance was compared when the proposed clustering feature selection approach and the standard feature selection were applied.

Radom forest achieved the highest accuracy of 95.91% higher than similar work compared. Furthermore, using clustering as a feature selection method improved the Sensitivity of the model from 73.83% to 98.79%. Sensitivity (recall) is the most important measure of classifier performance when detecting promoters’ accounts that have spam-like behavior.

The method applied is novel, more testing is needed in other datasets before generalizing its results.

The model applied can be used by Saudi authorities to report on the accounts that sell sick-leaves online.

The research is proposing a new way textual clustering can be used in feature selection.

The purpose of this paper is to investigate and report students’ perception of service quality in a university by examining the perceptual context of service quality with respect to students’ loyalty behavior, image of the university and culture/values.

A research framework is developed for quality assessment with three hypotheses. A questionnaire with 65 instruments was used for gathering the required data for the analysis. The questionnaire was sent through email to all engineering students. The analysis included descriptive statistics, reliability analysis, gap analysis and hypotheses tests. Seven dimensions of service quality were identified: the original dimensions of the SERVQUAL, namely, reliability, responsiveness, assurance, empathy and tangibles. Two additional dimensions image and culture/value were added for the research to understand perceived service quality and loyalty.

The results provide a significant positive correlation between service quality and student's loyalty. It also shows that there is statistically significant relation between the image of the institution and the perceived service quality, and culture/values of the students in the institution and perceived service quality.

This study used data collected from a survey in the university in a given period.

The findings indicate that to provide quality education, meeting students’ needs, wants and expectations of services quality should be carefully understood and addressed. Management also needs to consider factors such as corporate image and culture/value, as they have the ability to heavily impact the type of services provided by the institution.

The findings presented in this paper fill the gap in the current literature by providing empirical knowledge on the quality of service assessment and customer satisfaction in the higher education context. The study is the first of its kind in Qatar’s context and provides opportunities for higher institutions to focus more on current students’ services. This can lead to an increased brand value representing one of the premier institutes of higher education in the Middle East Gulf Region.