A CBR for integrating sentiment and stress analysis for guiding users on social network sites. (1st December 2022) - Aguado, G.; Julian, V.; Garcia-Fornes, A.; Espinosa, A.
Abstract: This work presents a Case-Based Reasoning (CBR) module that integrates sentiment and stress analysis on text and keystroke dynamics data with context information of users interacting on Social Network Sites (SNSs). The context information used in this work is the history of positive or negative messages of the user, and the topics being discussed on the SNSs. The CBR module uses this data to generate useful feedback for users, providing them with warnings if it detects potential future negative repercussions caused by the interaction of the users in the system. We aim to help create a safer and more satisfactory experience for users on SNSs or in other social environments. In a set of experiments, we compare the effectiveness of the CBR module to the effectiveness of different affective state detection methods. We compare the capacity to detect cases of messages that would generate future problems or negative repercussions on the SNS. For this purpose, we use messages generated in a private SNS, called Pesedia. In the experiments in the laboratory, the CBR module managed to outperform the other proposed analyzers in almost every case. The CBR module was fine-tuned to explore its performance when populating the case base with different configurations. Highlights: A Case-Based Reasoning engine predicts outcomes using affective states and context. The proposed hypothesis is that the CBR will outperform state detection methods. The engine outperformed affective state detection methods in predicting outcomes. This proposal aims to prevent risks of users navigating on-line social environments.