With the tremendous increase in the use of social network sites like Twitter and Facebook, online community is exchanging information in the form of opinions, sentiments, emotions, and intentions, which reflect their affiliations and aptitude towards an entity, event and policy [1-3]. The propagation of extremist content has also been increasing and being considered as a serious issue in the recent era due to the rise of militant groups such as Irish Republican Army, Revolutionary Armed Forces of Colombia (FARC), Al Quaeda, ISIS (Daesh), Al Shabaab, Taliban, Hezbollah and others [4]. These groups have spread their roots not only at the community levels but also their networks are gaining control of social networking sites [5]. These networking sites are vulnerable and approachable platforms for the group strengthening, propaganda, brainwashing, and fundraising due to its massive impact on public sentiments and opinions. Opinions expressed on such sites give an important clue about the activities and behavior of online users. Detection of such extremist content is important to analyze user sentiment towards some extremist group and to discourage such associated unlawful acts. It is also beneficial in terms of classifying user's extremist affiliation by filtering tweets prior to their onward transmission, recommendation or training AI Chatbot from tweets [6].

Learning using the Internet or training through E-Learning is growing rapidly and is increasingly favored over the traditional methods of learning and teaching. This radical shift is directly linked to the revolution in digital computer technology. The revolution propelled by innovation in computer technology has widened the scope of E-Learning and teaching, whereby the process of exchanging information has been made simple, transparent, and effective. The E-Learning system depends on different success factors from diverse points of view such as system, support from the institution, instructor, and student. Thus, the effect of critical success factors (CSFs) on the E-Learning system must be critically analyzed to make it more effective and successful. This current paper employed the analytic hierarchy process (AHP) with group decision-making (GDM) and Fuzzy AHP (FAHP) to study the diversified factors from different dimensions of the web-based E-Learning system. The present paper quantified the CSFs along with its dimensions. Five different dimensions and 25 factors associated with the web-based E-Learning system were revealed through the literature review and were analyzed further. Furthermore, the influence of each factor was derived successfully. Knowing the impact of each E-Learning factor will help stakeholders to construct education policies, manage the E-Learning system, perform asset management, and keep pace with global changes in knowledge acquisition and management.

Higher education institutions, like nearly all organizations, need to implement information management systems that enable them to handle routine operations easily and, at the same time, generate many types of standardized and ad hoc reports. Higher professional education (HPE) institutions face unique challenges when implementing their computer-based information management systems. Electronic records management systems (ERMSs) help manage the extensive information needed to plan and make well-informed decisions. ERMS is a fairly new addition to organizations, and those organizations are still learning how to use them effectively. Unfortunately, some organizations are still slow to adopt these systems. With this in mind, this paper proposes a framework that identifies the key factors that influence HPEs in adopting their own ERMS. The framework developed in this paper is based on two other models: the unified theory of acceptance and use of technology (UTAUT) and technology-organization-environment (TOE). The questionnaires we distributed to 364 respondents in the HPE sector to collect the views of as many stakeholders as possible. These survey responses led the study to propose a framework that identifies the critical factors that influence the adoption of ERMSs in HPEs. This framework is expected to guide HPE institutions in understanding the most essential factors (individual, technological, and environmental) that must be addressed to adopt an ERMS.INDEX TERMS Information management systems, electronic records management, computer-based information systems, records, higher education institutions, information and educational field.

Although electronic records management system (ERMS) is important in bringing about the productivity of organizations, majority of them refuse to implement it, while a few embark on implementing it blindly, without guidance, which often results in failure. This paper, therefore, proposed a model for the ERMS adoption to support the productivity and performance of higher professional education (HPE) institutions in the Yemeni context. This paper used the unified theory of acceptance and use of technology (UTAUT) and a mixed explanatory approach to gather quantitative and qualitative data. Data were then analyzed through the use of SPSS 21, with SEM and Smart PLS V3 software used to test the proposed model. The model was also confirmed by five experts who were interviewed to obtain qualitative data. Based on the analysis results, all the fit indices met the recommended values range that assumed the acceptability of the developed model. The model was found to be of a good fit, and the theory upon which the model was developed was stable. The quantitative findings showed that performance expectancy, effort expectancy, social influence, facilitating conditions, policy, and training have a significant relationship with the ERMS adoption, which in return has a significant relationship with HPE organizations' productivity. This was supported by the qualitative results, confirming the theoretical study and contributing to the understanding of the ERMS adoption among HPEs. Such adoption ensures educational institutions' productivity.

The classification of emotional states from poetry or formal text has received less attention by the experts of computational intelligence in recent times as compared to informal textual content like SMS, email, chat, and online user reviews. In this study, an emotional state classification system for poetry text is proposed using the latest and cutting edge technology of Artificial Intelligence, called Deep Learning. For this purpose, an attention-based C-BiLSTM model is implemented on the poetry corpus. The proposed approach classifies the text of poetry into different emotional states, like love, joy, hope, sadness, anger, etc. Different experiments are conducted to evaluate the efficiency of the proposed system as compared to other state-of-art methods as well as machine learning and deep learning methods. Experimental results depict that the proposed model outperformed the baselines studies with 88% accuracy. Furthermore, the analysis of the statistical experiment also validates the performance of the proposed approach.