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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 study aims to present metaGraphos, a crowdsourcing system that aids in the transcription and semantic enhancement of scanned documents by using a pool of volunteers or people willing to participate in exchange for a financial reward.

The metaGraphos can be used in circumstances where optical character recognition fails to produce satisfactory results, semantic tagging or assigning thematic headings to texts is considered necessary or even when ground-truth data has to be collected in raw form.

The system automatically provides a Web-based interface comprising a static HTML page and JavaScript code that displays the scanned images of the document, coupled with the corresponding incomplete texts side by side, allowing users to correct or complete the texts in parallel.

By assisting the parallel transcription and the semantic enhancement of difficult scanned documents, the system further reveals the hidden cultural wealth and aids in knowledge dissemination, a fact that contributes significantly to the academic-scientific dialog and feedback.

Individual researchers, libraries and organizations in general may benefit from the system because it is cost-effective, practical and simple to set up client–server architecture that provides a reliable way to transcribe texts or revise transcriptions on a large scale.

Together, learning objects (LOs) and e-pedagogical practices have the potential to improve the performance of e-learning systems in several ways. They can make e-learning more personalised and adaptable, providing students with a more engaging and effective learning experience.

The development of LOs and e-pedagogical practices have significantly influenced and changed the performance of e-learning systems. LOs are self-contained, reusable units of instructional content that create instructional materials, such as online courses, tutorials and assessments. They provide a flexible and modular approach to designing and delivering e-learning content, allowing educators to easily customise and adapt their materials to the needs of their students. e-pedagogical practices refer to the use of technology to enhance and support the teaching and learning process. They include strategies such as online collaboration, gamification and adaptive learning to improve student engagement, motivation and achievement.

To achieve this objective, this study consists of two main phases. First, the authors extract metadata from LOs using latent semantic analysis algorithms, which are considered a strong tool in web-mining exploration techniques. Second, they identify LOs according to a particular form of similarity using fuzzy c-means (FCM) algorithms. To improve classification accuracy, the FCM is used as a clustering algorithm.

Finally, in order to assess the effectiveness of LOs with FCM, a series of experimental studies using a real-world dataset are conducted. The results of this study indicate that the proposed approach exceeds the traditional approach and produces good results.

There is a strong prerequisite for organizations to analyze customer review behavior to evaluate the competitive business environment. The purpose of this study is to analyze and predict customer reviews of halal restaurants using machine learning (ML) approaches.

The authors collected customer review data from the Yelp website. The authors filtered the reviews of only halal restaurants from the original data set. Following cleaning, the filtered review texts were classified as positive, neutral or negative sentiments, and those sentiments were scored using the AFINN and VADER sentiment algorithms. Also, the current study applies four machine learning methods to classify each review toward halal restaurants into its sentiment class.

The experiment showed that most of the customer reviews toward halal restaurants were positive. The authors also discovered that all of the methods (decision tree, linear support vector machine, logistic regression and random forest classifier) can correctly classify the review text into sentiment class, but logistic regression outperforms the others in terms of accuracy.

The results facilitate halal restaurateurs in identifying customer review behavior.

Sentiment and emotions, according to appraisal theory, form the basis for all interactions, facilitating cognitive functions and supporting prospective customers in making sense of experiences. Emotion theory also describes human affective states that determine motives and actions. The study looks at how potential customers might react to a halal restaurant’s consensus on social media based on reviewers’ opinions of halal restaurants because emotions can be conveyed through reviews.

This study applies machine learning approaches to analyze and predict customer sentiment based on the review texts toward halal restaurants.

Text classification is a widely accepted and adopted technique in organizations to mine and analyze unstructured and semi-structured data. With advancement of technological computing, deep learning has become more popular among academicians and professionals to perform mining and analytical operations. In this work, the authors study the research carried out in field of text classification using deep learning techniques to identify gaps and opportunities for doing research.

The authors adopted bibliometric-based approach in conjunction with visualization techniques to uncover new insights and findings. The authors collected data of two decades from Scopus global database to perform this study. The authors discuss business applications of deep learning techniques for text classification.

The study provides overview of various publication sources in field of text classification and deep learning together. The study also presents list of prominent authors and their countries working in this field. The authors also presented list of most cited articles based on citations and country of research. Various visualization techniques such as word cloud, network diagram and thematic map were used to identify collaboration network.

The study performed in this paper helped to understand research gaps that is original contribution to body of literature. To best of the authors' knowledge, in-depth study in the field of text classification and deep learning has not been performed in detail. The study provides high value to scholars and professionals by providing them opportunities of research in this area.