This thesis work aimed to understand whether the nationality of a person influences the answers of Large Language Models when they are asked to assign an emotion to an explicit situation. Furthermore, a study about the impact of differentiating the prompt and the analysis of the benefits of using country information in the emotion assignment task was conducted. In order to use a different strategy and improve the classification metrics, clustering techniques were performed. The final results show that country information is useful in helping the model assign an emotion and solve the indefiniteness problems when a generative model is used.