Short text classification of social groups focused on courses - Candido, Antonio Leandro Martins; Maia, Jose Everardo Bessa (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2025: Anais do XXII Encontro Nacional de Inteligência Artificial e ComputacionalDO - 10.5753/eniac.2025.10530, 2025)
The ubiquitous possession of communication devices enhances a Social IoT whose most visible face is Social Networks. Social networks, also called relationship networks, are an important source for collecting unbiased feelings or mental states of groups of students in relation to a course in which they participate. These groups in social networks, often isolated and without the knowledge of the school administration, function as a discussion forum and the analysis of the texts posted there can enhance educational management with preventive measures to mitigate factors of course failure. This work develops the classification of sentiment in texts of groups of students with a view to detecting the need for intervention by educational management. The accuracy of the method is reinforced by a domain adaptation technique to take advantage of labeled data from other related domains. The results obtained are compatible with the state of the art of sentiment classification in other application domains.