Opinion Identification using a Conversational Large Language Mode

Abstract

The paper focuses on testing the use of conversational Large Language Model (LLM), in particular
chatGPT and Google models, instructed to assume the role of linguistics experts to produce opinions. In
contrast to knowledge/evidence-based objective factual statements, opinions are defined as subjective statements about animates, things, events or properties in the context of an Opinion (Speech) Event in a social cultural context. Taxonomy distinguishes explicit (direct/indirect) and implicit opinions (positive, negative, ambiguous, or balanced). Contextually richer prompts at the LLMs training phase are shown to be needed to deal with variants of implicit opinion scenario types.