## 4 MAS-Based Prevention and Recommendation Systems Review
These four works were selected for assessing the usefulness of MAS technologies for detecting the state of the user to address the different topics mentioned. For the case of References [47; 48], the former addresses sentiment and stress analysis on text data for guiding users, while the later uses both text and keystroke data combined for this purpose. A MAS with agents that apply reinforcement learning algorithms to learn the sentiment pertaining to specific keywords was presented in Reference [49]. Agents collectively learn this sentiment since each agent processes its assigned subset of data. Experiments were conducted on abstracts from PubMed related to muscular atrophy, Alzheimer's disease, and diabetes, and results show that the system was able to learn the sentiment score related to specific keywords. A MAS architecture with a set of agents that work with opinion data from different SNSs is proposed in Reference [50]. The proposed system has an agent extracting opinion about a product from Twitter, another from Wikipedia, and another from Facebook. All agents compute sentiment using machine learning techniques and are able to communicate between them by using a blackboard. In this way, they can generate a more complete opinion about the product with sentiment computed on additional features by other agents. In Reference [51] ActoDatA (Actor Data Analysis), an actor-based software library for building distributed data analysis applications is presented, and a prototype is implemented. This library provides a multi-agent architecture and different implementations of five agent types, which are acquireer, preprocessor, engine, controller, and master. Each agent acts as a wrapper for components that perform the different tasks of a data analysis application. A framework built as an agent-based model is presented in Reference [52]. The framework is used to understand and predict the emergence of collective emotions based on interactions between agents, who have individual emotional states. For helping enterprises be aware of their customer's opinions about products or services, an agent-based social framework that extracts reviews from social media is presented in Reference [53]. Data analysis and storage are performed by using a framework based on Hadoop MapReduce and HBase, respectively, which allows the efficient manipulation of big amounts of data, which is the case for the task of opinion mining from SNS data. As can be seen, the MAS architecture can be useful to model the emotion of users or mine opinions in different ways that are related to computing this emotion or opinion in a distributed way. It can help compute collectively emotion using different sources of data, can help build distributed data analysis applications, predict the emergence of collective emotions based on interactions between agents with individual emotions, or create big data distributed applications to perform opinion mining with big volumes of data, such as SNS data. Therefore, the emotion of users can be computed in different ways and in a distributed architecture using MAS technologies and machine learning or other detection technologies, and then use this information to guide users, recommend, and prevent risks and potential issues in SNSs or other environments with emotional entities.