Sensitive Content Recognition in Social Interaction Messages

Online social networks are a predominant medium for social interaction where people communicate in a way similar to what they do in real life. User communication comes mainly in forms of textual data which are rich in personal information, opinions and sentiments. The automatic recognition of sensitive content in texts is quite important for a number of reasons. In this work, we study the dimensions of sensitive content recognition and we examine the performance of various machine learning methods for sensitive data recognition in text. Understanding the key features of sensitive content can assist in formulating more efficient user-centric interaction frameworks too that secure users’ privacy, promote users' inclusion and enhance the diversity awareness of the online society. Also, another part of this work focuses on the models’ explainability where the integration of LIME and SHAP offer insight on features that are consistent and robust predictors of sensitive content

Sentiment Analysis of Teachers Using Social Information in Educational Platform Environments

© 2020 World Scientific Publishing Company. Electronic version of an article published as International Journal on Artificial Intelligence Tools, Vol. 29, No. 02, 2040004 (2020): https://doi.org/10.1142/S0218213020400047.Learners’ opinions constitute an important source of information that can be useful to teachers and educational instructors in order to improve learning procedures and training activities. By analyzing learners’ actions and extracting data related to their learning behavior, educators can specify proper learning approaches to stimulate learners’ interest and contribute to constructive monitoring of learning progress during the course or to improve future courses. Learners-generated content and their feedback and comments can provide indicative information about the educational procedures that they attended and the training activities that they participated in. Educational systems must possess mechanisms to analyze learners’ comments and automatically specify their opinions and attitude towards the courses and the learning activities that are offered to them. This paper describes a Greek language sentiment analysis system that analyzes texts written in Greek language and generates feature vectors which together with classification algorithms give us the opportunity to classify Greek texts based on the personal opinion and the degree of satisfaction expressed. The sentiment analysis module has been integrated into the hybrid educational systems of the Greek school network that offers life-long learning courses. The module offers a wide range of possibilities to lecturers, policymakers and educational institutes that participate in the training procedure and offers life-long learning courses, to understand how their learners perceive learning activities and specify what aspects of the learning activities they liked and disliked. The experimental study show quite interesting results regarding the performance of the sentiment analysis methodology and the specification of users’ opinions and satisfaction. The feature analysis demonstrates interesting findings regarding the characteristics that provide indicative information for opinion analysis and embeddings combined with deep learning approaches yield satisfactory results.Peer reviewe

A Comparative Performance Evaluation of Algorithms for the Analysis and Recognition of Emotional Content

Sentiment Analysis is highly valuable in Natural Language Processing (NLP) across domains, processing and evaluating sentiment in text for emotional understanding. This technology has diverse applications, including social media monitoring, brand management, market research, and customer feedback analysis. Sentiment Analysis identifies positive, negative, or neutral sentiments, providing insights into decision-making, customer experiences, and business strategies. With advanced machine learning models like Transformers, Sentiment Analysis achieves remarkable progress in sentiment classification. These models capture nuances, context, and variations for more accurate results. In the digital age, Sentiment Analysis is indispensable for businesses, organizations, and researchers, offering deep insights into opinions, sentiments, and trends. It impacts customer service, reputation management, brand perception, market research, and social impact analysis. In the following experimental research, we will examine the Zero-Shot technique on pre-trained Transformers and observe that, depending on the Model we use, we can achieve up to 83% in terms of the model’s ability to distinguish between classes in this Sentiment Analysis problem

Examining the Impact of a Gamified Entrepreneurship Education Framework in Higher Education

Entrepreneurship education constitutes a top priority in policy agendas across the globe as a means to promote economic growth, fight unemployment and create social capital. An important premise of entrepreneurship education is that it can be learned and students can be taught to formulate entrepreneurial mentality, skills and competencies, something that can result in the formulation of startups and business initiatives. Given the importance of entrepreneurship, the necessity to formulate efficient entrepreneurship education frameworks and training programs arise. In this work, we present the design of an entrepreneurship educational environment that is based on learning in 3D virtual worlds. Innovative 3D virtual reality technologies were utilized to provide immersive and efficient learning activities. Various topics of entrepreneurship education courses were designed and formulated to offer students the opportunity to obtain theoretical knowledge of entrepreneurship. The 3D virtual reality educational environment utilizes pedagogical approaches that are based on gamification principles, allowing students to study in immersive ways as well as in game-based learning activities on real challenges that can be found in business environments. The game-based learning activities can help students gain necessary skills, helping them to tackle everyday obstacles on their entrepreneurial pathways. An experimental study was performed to explore the learning efficiency of the environment and the gamified learning activities as well as assess their learning impact on student’s motivation, attitude, and overall learning experience. The evaluation study revealed that the framework offers efficient gamified learning activities that increase students’ motivation and assist in the formulation of entrepreneurship mentality, skills and competencies

The Effectiveness of Embodied Pedagogical Agents and Their Impact on Students Learning in Virtual Worlds

Over the last years, the successful integration of virtual reality in distance education contexts has led to the development of various frameworks related to the virtual learning approaches. 3D virtual worlds are an integral part of the landscape of education and demonstrate novel learning possibilities that can open new directions in education. An important aspect of virtual worlds relates to the intelligent, embodied pedagogical agents that are employed to enhance the interaction with students and improve their overall learning experience. The proper design and integration of embodied pedagogical agents in virtual learning environments are highly desirable. Although virtual agents constitute a vital part of virtual environments, their exact impact needs are yet to be addressed and assessed. The aim of the present study is to thoroughly examine and deeply understand the effect that embodied pedagogical agents have on the learning experience of students as well as on their performance. We examine how students perceive the role of pedagogical agents as learning companions during specific game-based activities and the effect that their assistance has on students’ learning. A concrete experimental study was conducted in AVARES, a 3D virtual world educational environment that teaches the domain of environmental engineering and energy generation. The results of the study point out that embodied pedagogical agents can improve students’ learning experience, enhance their engagement with learning activities and, most of all, improve their knowledge construction and performance

Evaluating Deep Learning Techniques for Natural Language Inference

Natural language inference (NLI) is one of the most important natural language understanding (NLU) tasks. NLI expresses the ability to infer information during spoken or written communication. The NLI task concerns the determination of the entailment relation of a pair of sentences, called the premise and hypothesis. If the premise entails the hypothesis, the pair is labeled as an “entailment”. If the hypothesis contradicts the premise, the pair is labeled a “contradiction”, and if there is not enough information to infer a relationship, the pair is labeled as “neutral”. In this paper, we present experimentation results of using modern deep learning (DL) models, such as the pre-trained transformer BERT, as well as additional models that relay on LSTM networks, for the NLI task. We compare five DL models (and variations of them) on eight widely used NLI datasets. We trained and fine-tuned the hyperparameters for each model to achieve the best performance for each dataset, where we achieved some state-of-the-art results. Next, we examined the inference ability of the models on the BreakingNLI dataset, which evaluates the model’s ability to recognize lexical inferences. Finally, we tested the generalization power of our models across all the NLI datasets. The results of the study are quite interesting. In the first part of our experimentation, the results indicate the performance advantage of the pre-trained transformers BERT, RoBERTa, and ALBERT over other deep learning models. This became more evident when they were tested on the BreakingNLI dataset. We also see a pattern of improved performance when the larger models are used. However, ALBERT, given that it has 18 times fewer parameters, achieved quite remarkable performance

Examining the Impact of Pedagogical Agents on Students Learning Experience in Virtual Worlds

Virtual worlds constitute an important part of the educational landscape that possesses unique learning capabilities and opens up new horizons in education. A vital part of 3D virtual worlds concerns the intelligent pedagogical agents that are integrated in the environments and aim to improve the interaction with the users and enhance their learning experience. The main purpose of this study is to assess the impact that the pedagogical agents have on students\u27 engagement and learning experiences during training activities in virtual worlds. Specifically, we examine how students perceive the role of pedagogical agents as learning companions during specific exercises and activities and the impact that the guidance offered via pedagogical agents has on students\u27 engagement and learning. In this regard, an experimental evaluation study was designed and performed in a 3D virtual world educational environment that is used to assist students in learning aspects of environmental engineering. The results of the study show that the assistance and the help offered to students via pedagogical agents have great impact on students\u27 engagement and improved their learning experiences