Text Mining Data - SET

Emotional classification (valence) in textual data has proved to be central to human experience analysis and natural language processing (NLP). This study implements a text mining model and algorithm - TM-EV (Text Mining for Emotional Valence Analysis) - that determines the impact of emotional valence (EV) shown by undergraduate students in their feedback (n=665860) during the program (pre- and post-course to determine its relationship with the learning outcome and performance. The method is grounded on appraisal theories and component process models (CPM) that study degree of pleasantness or goal achievement as an effect of valence judgements. The model (TM-EV) identifies top terms in the students’ data using Corpus feature selection and Term document matrix libraries in R software. It further utilizes the EV scores (quantified data) extracted from the (textual) data to statistically test the association and effect it has with the Evaluation periods and Academic level of the students. Data analysis was done using Sentiment Analysis libraries (sentimentr, syuzhet, pander) in R, and Statistical Analysis methods (Multiple Linear Regression, ANOVA, ANCOVA) in IBM SPSS v30. The results show that individually the Evaluation periods and Academic level do not directly impact EV scores of the students (p>0.05), but a combined interaction effect of the two factors impacts the EV scores (p=0.003). The paper sheds light on the pedagogical and socio-technical implications of the study’s findings toward achieving improved learning outcomes and sustainable educational practices.