The paper tests conversational Large Language Models, instructed to produce stance expression types (affective, relational, epistemic, and moral) and their contexts in Opinion (Speech) Events (Lewandowska-Tomaszczyk, Barbara, Chaya Liebeskind, Anna Baczkowska, Jurate Ruzaite, Ardita Dylgjeri, Ledia Kazazi & Erika Lombart 2023. Opinion events: Types and opinion markers in English social media discourse. Lodz Papers in Pragmatics 19(2). 447–481). In the first part an opinion taxonomy proposed in (Lewandowska-Tomaszczyk, Barbara, Chaya Liebeskind, Anna Baczkowska, Jurate Ruzaite, Ardita Dylgjeri, Ledia Kazazi & Erika Lombart 2023. Opinion events: Types and opinion markers in English social media discourse. Lodz Papers in Pragmatics 19(2). 447–481) is discussed in terms of Explicit (direct or indirect) and Implicit opinionated texts, categorized as positive, negative, ambiguous, or balanced. The further part discusses our previous attempts at Explicit (direct/indirect) and Implicit opinion type generation, performed by means of a series of prompts with LLMs (ChatGPT and Gemini) (Liebeskind, Chaya & Barbara Lewandowska-Tomaszczyk. 2024a. Opinion identification using a conversational large language model. In FLAIRS conference Proceedings. Florida, Liebeskind, Chaya & Barbara Lewandowska-Tomaszczyk. F 2024b. Navigating opinion space: A Study of explicit and implicit opinion generation in language models. Santiago de Compostella: EAIS conference publication), while this paper presents further LLM experiments with chatGPT and Gemini as well as their results, based on the analysis of stance expression types, which lead to increased success in opinion context generation.