The present work is a study of the detection of negative affective or emotional states, the high-stress levels that people have using social network sites (SNSs), and the effect that this negative state or stress level has on the repercussions of posted messages. We aim to discover to what extent a user that has a state detected as negative by an analyzer (Sentiment analyzer and Stress analyzer) can affect other users and generate negative repercussions, and also determine whether it is more suitable to predict a future negative situation using different analyzers. We propose two different methods for creating a combined model of sentiment and stress, and we use them in our experimentation to discern which one is more suitable for predicting future negative situations that could arise from the interaction between users, and in what context. Additionally, we designed a Multi-Agent System (MAS) that integrates the analyzers to protect or advise users on a SNS. We have conducted this study to help build future systems that prevent negative situations where a user that has a negative state creates a repercussion in the SNS. This can help users avoid getting into a bad mood or help avoid privacy issues (e.g. a user that has a negative state posting information that the user does not really want to post).

目前的工作是对负面情绪或情绪状态的检测，人们使用社交网站（SNS）的高压力水平以及这种负面状态或压力水平对已发布消息的影响的研究。我们旨在发现被分析仪（情绪分析仪和压力分析仪）检测为负面状态的用户在多大程度上会影响其他用户并产生负面影响，并确定是否更适合使用以下方法预测未来的负面情况不同的分析仪。我们提出了两种不同的方法来创建情绪和压力的组合模型，并在实验中使用它们来识别哪种方法更适合预测用户之间以及在何种情况下的交互作用可能引起的未来负面情况。此外，我们设计了一个多代理系统（MAS），该系统集成了分析仪以保护SNS或向用户提供建议。我们进行了这项研究，以帮助构建将来的系统，以防止出现负面情况的用户处于负面状态，从而在SNS中产生反响。这可以帮助用户避免陷入不良情绪或避免隐私问题（例如，处于负面状态的用户发布了该用户实际上不想发布的信息）。