During the pandemic, when people needed to physically distance, social media platforms have been one of the outlets where people expressed their opinions, thoughts, sentiments, and emotions regarding the pandemic situation. The core object of this research study is the sentiment analysis of peoples’ opinions expressed on Facebook regarding the current pandemic situation in low-resource languages. To do this, we have created a large-scale dataset comprising of 10,742 manually classified comments in the Albanian language. Furthermore, in this paper we report our efforts on the design and development of a sentiment analyser that relies on deep learning. As a result, we report the experimental findings obtained from our proposed sentiment analyser using various classifier models with static and contextualized word embeddings, that is, fastText and BERT, trained and validated on our collected and curated dataset. Specifically, the findings reveal that combining the BiLSTM with an attention mechanism achieved the highest performance on our sentiment analysis task, with an F1 score of 72.09%.

In this paper, we present our design approach for bridging outdoors and indoors learning activities with the support of mobile and positioning technologies. In order to illustrate these research efforts we describe the outcomes of two trials we have conducted with more than 50 elementary school children. The activities presented in this paper aspire at supporting the notion of situated learning with mobile and positioning technologies to promote new ways of collaboration based on the users' learning context. The results of our experiments indicate that children enjoyed learning where mobile devices are used in situ, supporting the learning activities in the context of which they are taking place.

Recent advances in mobile, wireless, and sensor technologies provide new possibilities for supporting learning activities that can be spatially distributed and incorporate different physical and environmental sensory data. In this paper, we present our technical efforts in relation to the design and implementation of mobile and web applications that integrate sensory data used to support inquiry-based science learning. In order to test the validity of our solution and its functionality and novelty, we conducted a prototype experiment with high school students in the field of environmental sciences. The initial outcomes presented in this paper point towards the potential benefits of using sensor and mobile technologies with real-time geo-positioned data and visualizations, which may increase students' engagement, enabling them to conduct scientific inquiries and analyses in new ways.

This article presents a systematic literature review on hybrid recommendation systems (HRS) in the e-commerce sector, a field characterized by constant innovation and rapid growth. As the complexity and volume of digital data increases, recommendation systems have become essential in guiding customers to services or products that align with their interests. However, the effectiveness of single-architecture recommendation algorithms is often limited by issues such as data sparsity, challenges in understanding user needs, and the cold start problem. Hybridization, which combines multiple algorithms in different methods, has emerged as a dominant solution to these limitations. This approach is utilized in various domains, including e-commerce, where it significantly improves user experience and sales. To capture the recent trends and advancements in HRS within e-commerce over the past six years, we review the state-of-the-art overview of HRS within e-commerce. This review meticulously evaluates existing research, addressing primary inquiries and presenting findings that contribute to evidence-based decision-making, understanding research gaps, and maintaining transparency. The review begins by establishing fundamental concepts, followed by detailed methodologies, findings from addressing the research questions, and exploration of critical aspects of HRS. In summarizing and incorporating existing research, this paper offers valuable insights for researchers and outlines potential avenues for future research, ultimately providing a comprehensive overview of the current state and prospects of HRS in e-commerce.