Emotional Variance Analysis: A new sentiment analysis feature set for Artificial Intelligence and Machine Learning applications.

ABSTRACT: Sentiment Analysis (SA) is a category of data mining techniques that extract latent representations of affective states within textual corpuses. This has wide ranging applications from online reviews to capturing mental states. In this paper, we present a novel SA feature set; Emotional Variance Analysis (EVA), which captures patterns of emotional instability. Applying EVA on student journals garnered from an Experiential Learning (EL) course, we find that EVA is useful for profiling variations in sentiment polarity and intensity, which in turn can predict academic performance. As a feature set, EVA is compatible with a wide variety of Artificial Intelligence (AI) and Machine Learning (ML) applications. Although evaluated on education data, we foresee EVA to be useful in mental health prof

SUBMITTER: Tan L 

PROVIDER: S-EPMC9836260 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature