In a world characterized by rapid change, uncertainty, and increasing interconnectedness, there is a growing need for science to solve persistent and complex problems. One of these problems has a profound social impact and refers to the operational or behavioral deviations of security professionals and law enforcement officers, who, due to the inherent nature of their activities, are often exposed to a variety of acute stress and life-threatening situations. Such situations tend to force, besides quick decisions, correct judgments on the part of the professionals, who, precisely because of this, need to train in technical, operational, and psychological aspects constantly. However, police training is expensive, involves risks, and has little scenario flexibility. In many cases, besides being insufficient, it is also inefficient in terms of education and performance evaluation of the professional being trained.

This research started precisely from this point, and evaluating the exposed context, it intended to propose a solution that could contribute to solving the problems related to training security professionals and law enforcement agents. To this end, this research proposed a unique combination of technologies, techniques, and methods to contribute to the problem’s solution.

To address these challenges, I proposed a method to guide the development of Virtual Reality (VR) simulators that combine Biofeedback and Serious Games for specialized training of security professionals and law enforcement agents. The method considers User Experience as the predominant factor. I based the method on the Design Science Research methodology and developed a VR simulator prototype to generate knowledge to support the method’s proposition.

I submitted the method and prototype to three evaluation cycles, two with specialists and a third with a broader audience of 141 professionals and academics from 11 countries.

After analyzing the survey responses, I identified different levels of relevance of the 31 activities in each of the 7 cycles, determined by the number of activities defined as very relevant or highly relevant by the respondents. I believe that the proposed development method can help improve police training, making it more accessible, secure, and flexible.

The relationship between customers and companies has changed with the popularization of the Internet and mobile technologies. Comments about companies, products, or services, once restricted to friendship circles, are now shared constantly and prolifically on social networks and websites that receive customer opinions about their experiences. This phenomenon provides opportunities for discovering knowledge from these opinions but also challenges, considering that, given their nature and form, customer opinions consist of unstructured data, which in turn require specific processing. This research presented a framework for opinion mining to discover customer knowledge about their experiences in companies (restaurants) based on unstructured data extracted from social networks applicable to the reality of small and medium-sized companies. The social network addressed in this research was TripAdvisor, from which data from four companies (restaurants) was extracted using the web scraping technique.

The main results were the generation of summaries and graphic visualizations that helped to highlight knowledge about the relationships between various expressions and terms that were not obvious. These, in turn, were discovered from the analysis, which made it possible to find latent relationships between terms mentioned by different clients. The Sentiment Analysis combined with Topic Modeling revealed that the aspects most mentioned by customers refer to the food, the place, and the service, varying in intensity and polarity. The practical contribution of this work lies in the application of Text Mining to reveal patterns and enable the discovery of knowledge from customer opinions extracted from social networks. The framework used proved helpful as a tool for better understanding the customer, their expectations, and even their frustrations, thus generating knowledge about customers for the company’s benefit.