Aspect-based sentiment analysis (ABSA) is currently among the most vigorous areas in natural language processing (NLP). Individuals, private and government institutions are increasingly using media sources for decision making. In the last decade, aspect extraction has been the most essential phase of sentiment analysis (SA) to conduct an abridged sentiment classification. However, previous studies on sentiment analysis mostly focused on explicit aspects extraction with limited work on implicit aspects. To the best of our knowledge, this is the first systematic review that covers implicit, explicit, and the combination of both implicit and explicit aspect extractions. Therefore, this systematic review has been conducted to, 1) identify techniques used for extracting implicit, explicit, or both implicit and explicit aspects; 2) analyze the various evaluation metrics, data domains, and languages involved in the implicit and explicit aspect extraction in sentiment analysis from years 2008 to 2019; 3) identify the key challenges associated with the techniques based on the result of a comprehensive comparative analysis; and finally, 4) highlight the feasible opportunities for future research directions. This review can be used to assist novice and prominent researchers to understand the concept of both implicit and explicit aspect extractions in aspect-based sentiment analysis domain.

The role of information and communication technology (ICT) in education continues to serve the development of teaching and learning for most subjects, and the subject of Islamic studies is no exception. However, to date, a systematic literature review (SLR) on the role of ICT for this particular subject is lacking. Therefore, to facilitate the implementation and adoption of ICT, we focus on e-learning for the majority of subjects, including Islamic education and ICT applications, e.g. educational software and the Internet, which are gaining exponential importance with time. This SLR considered research works in the literature that have been published in the temporal range of June 2007 to June 2020. We derived 41 publications from an initial 301 candidate publications, by applying the inclusion and exclusion criteria in two distinct rounds. This rigorous review explored teachers' and students' opinions on the application of technology, and shed light on the challenges to date, as well as the potential opportunities and future research directions. We also performed Tweet analysis in an example of public opinion available on social media, e.g. Twitter, for learning education through ICT-based methods. This study delivers several implications for researchers and practitioners, and provides insight on state-of-the-art. Our findings suggest that ICT-based teaching methods for learning Islamic studies as a research context require more attention. Further empirical investigation is crucial to better understand the impact of ICT practices and use, especially in the context of Islamic studies.

A sentiment analysis of Arabic texts is an important task in many commercial applications such as Twitter. This study introduces a multi-criteria method to empirically assess and rank classifiers for Arabic sentiment analysis. Prominent machine learning algorithms were deployed to build classification models for Arabic sentiment analysis classifiers. Moreover, an assessment of the top five machine learning classifiers’ performances measures was discussed to rank the performance of the classifier. We integrated the top five ranking methods with evaluation metrics of machine learning classifiers such as accuracy, recall, precision, F-measure, CPU Time, classification error, and area under the curve (AUC). The method was tested using Saudi Arabic product reviews to compare five popular classifiers. Our results suggest that deep learning and support vector machine (SVM) classifiers perform best with accuracy 85.25%, 82.30%; precision 85.30, 83.87%; recall 88.41%, 83.89; F-measure 86.81, 83.87%; classification error 14.75, 17.70; and AUC 0.93, 0.90, respectively. They outperform decision trees, K-nearest neighbours (K-NN), and Naïve Bayes classifiers.

Aspect-based sentiment analysis (ABSA) is described as one of the most vibrant research areas over the last decade. However, due to the exponential increase in aspect-based sentiment researches, there is a massive interest in advanced explicit aspect extraction (EAE) techniques. This interest brings about a huge amount of literature in the EAE domain. This study aims to investigate and identify the existing approaches, techniques, types of research, quantity of publications, publication trends and demographics shaping the EAE research domain in the last decade (2009 - 2019). Accordingly, an evidence-based systematic methodology was adopted to effectively capture all the relevant studies. The main findings revealed that, 1) there is considerable and continuous rise of EAE research activities around different parts of the globe in the last five years, particularly Asia, Middle-East, and European countries; 2) EAE research has been very limited among African countries which need to be addressed due its role on business intelligence as well as semantic values; 3) three research facets were highlighted based on this study, i.e. solution research, validation research, and evaluation research, in which solution research gets the highest attention; and finally 4) the EAE challenges, as well as feasible future recommendations, were highlighted in this study.