## 6 Conclusions and Future Lines of Work  
Since we have discussed previous works in risk prevention, recommendation and user state detection, and highlighted the relations between them and potential uses for preventing users from suffering negative consequences from their interaction, and for helping improve future systems, following potential future works in three different lines will be discussed, which are:  
* Using current technologies in user state detection for creating improved user guiding systems in online environments. * Combining different technologies compatible with the architecture of SNSs with emotion detection techniques and testing their effectiveness in real-life scenarios as guiding or recommendation systems. * Improving user state detection techniques. Regarding future lines of work using current technologies in user state detection, it should be taken into account that automatic user state detection gives information to the system about a factor that directly influences decision making, and consequently the probability of incurring one of the risks that arise from the interaction between users. Therefore, it is desirable that a system that guides users navigating exploits the potential of the extensive amount of sentiment analysis techniques in the existing literature. Secondly, combining different sources of data has shown to help improve the system performance when detecting emotional states, and also using sentiment analysis together with stress analysis. In this way, future works could investigate new ways of combining different kinds of data in fusion approaches, and also use different analysis such as stress analysis together with sentiment analysis to try to improve the system performance. It could allow researchers to discover a correlation between different variables such as stress levels of users, sentiment or other factors, and at the same time it might improve the system performance to use detection of multiple aspects of the user state to perform a guiding or recommendation, therefore it is an interesting possibility to test. Another potential branch of future lines of work where there is room for improvement is investigating the effect of a combination of different technologies compatible with the architecture of SNSs with emotion detection techniques, and the effectiveness of such systems in different real-life scenarios (to guide users by analyzing their data and giving them feedback). CBR techniques,as has been reviewed, can be used to work together with sentiment analysis techniques for example exploiting different sentiment lexicons. Since CBR systems can be integrated easily into a SNS as a guiding module that helps users by monitoring their interactions and giving them feedback based on the different characteristics of the interaction and user states, they are potential candidates for designing user guiding systems that create a more satisfactory and safe user experience. Moreover, recommendation systems that use system data to give recommendations to users could potentially be improved, using, for example, persuasion techniques and sentiment or stress analysis. Finally, MAS-based approaches can fit in the SNS architecture, for example assigning agents to users and different system tasks to other agents. These systems can work together with user state detection techniques as user guiding and recommendation systems, by using the data collected by the automatic user state detection when users interact to give advice or recommendations. Therefore investigating the effect of combining different user state detection techniques with MAS-based approaches might improve the performance of the system as a guiding or recommendation system.