Contribuições da relação de oposição adjetival para o mapeamento de sentimentos em plataformas online de ensino

Abstract

The aim of this dissertation was to describe semantically adjectives opposition of sentiments domain in the Distance Education context. The purpose was to enrich an emotion lexicon which will be used as a database for a sentiment analyzer to identify automatically sentiments expressed by students on the open source learning platform Moodle. One of the justifications for building a sentiment analyzer applied to the distance education context is the belief that one of the factors that contribute to its success is the capacity of the teacher/tutor to identify as quickly as possible how students are feeling using the platform. Students’ declarations are diffused in several tolls in the platform and for this reason their identification and a quick response to students are less effective what can influence the evasion in courses and disciplines on a distance basis. This study is interdisciplinary, founded in Cognitive Linguistics (Cruse, 1986; 2000), interaction with Automatic Processing of Natural Language, from the Computacional Lexical Semantic Theory in the Sentiment Analysis (Pang e Lee, 2008; Liu, 2012). As an interdisciplinary study, the methodology comprehend three domains which complement one another: linguistic, computational-linguistic and computational (Dias-da-Silva, 1996; 1998; 2003). At regarding linguistic domains the emotion according to the componential psychologic approach from Scherer (1994; 2000; 2005; 2013), the Geneva Emotion Wheel (Scherer, 2005) and the linguistic phenomenon of opposition (Lyons, 1977; Cruse, 1986; 2000; Murphy, 2003) were studied. At concerning the computational linguistic domain a formalizable description of adjectives was proposed with respect to the opposition theory studied and the Geneva Emotion Wheel. The computational domain will be done by a computer science team from Unisinos, who are working with us in the project “MAS-EaD: Automatic sentiment mining in distance education: building an emotion lexicon”, defrayed by FAPERGS. The findings of this investigation showed that the literature presents two types of opposition, complementary and antonym, but only antonym cases were found in our corpus. Thereby, the opposition relation is the main relation for the Sentiment Analysis, because it identifies opposite sentiments. Besides, the opposition relation is important to organize sentiment polarities of the Geneva Emotion Wheel of Scherer.