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User-generated content on social media platforms has reached big data levels. Sentiment analysis of this data provides opportunities to gain valuable insights into any domain. However, analyzing real-world data may confront the challenge of class imbalance, which can adversely affect the generalization ability of models due to majority class overfitting. Therefore, having an efficient model that manages any scenario of imbalanced data is practically needed. In this light, this work proposes different models based on studying the impact of data quality and transfer learning through pre-trained embeddings on boosting minority class detection. The proposed models are tested on imbalanced datasets related to social media and education. The experimental results highlight the effectiveness of Wor2vec, Glove, and Fasttext embeddings with preprocessed data. In contrast, BERT embeddings present better results with no-preprocessed data. Furthermore, in comparison with other methods, the best-performing model resulting from this study shows outperformance with notable improvements. [ABSTRACT FROM AUTHOR]

• Atmosphere is a three-tier software architecture for IoT edge-fog-cloud computing. • Atmosphere benefits from Complex Event Processing and Agent-Oriented Software. • Two-way communications are allowed among the three tiers of the architecture. The Internet of Things (IoT) has grown significantly in popularity, accompanied by increased capacity and lower cost of communications, and overwhelming development of technologies. At the same time, big data and real-time data analysis have taken on great importance and have been accompanied by unprecedented interest in sharing data among citizens, public administrations and other organisms, giving rise to what is known as the Collaborative Internet of Things. This growth in data and infrastructure must be accompanied by a software architecture that allows its exploitation. Although there are various proposals focused on the exploitation of the IoT at edge, fog and/or cloud levels, it is not easy to find a software solution that exploits the three tiers together, taking maximum advantage not only of the analysis of contextual and situational data at each tier, but also of two-way communications between adjacent ones. In this paper, we propose an architecture that solves these deficiencies by proposing novel technologies which are appropriate for managing the resources of each tier: edge, fog and cloud. In addition, the fact that two-way communications along the three tiers of the architecture is allowed considerably enriches the contextual and situational information in each layer, and substantially assists decision making in real time. The paper illustrates the proposed software architecture through a case study of respiratory disease surveillance in hospitals. As a result, the proposed architecture permits efficient communications between the different tiers responding to the needs of these types of IoT scenarios. [ABSTRACT FROM AUTHOR]