Opinion mining plays a critical role in understanding public sentiment and preferences, particularly in the context of political elections. Traditional polling methods, while useful, can be expensive and less scalable. Social media offers an alternative source of data for opinion mining but presents challenges such as noise, biases, and platform limitations in data collection. In this paper, we propose a novel approach for opinion mining, utilizing YouTube's auto-generated captions from public interviews as a data source, specifically focusing on the 2023 Turkish elections as a case study. We introduce an opinion mining framework using ChatGPT to mass-annotate voting intentions and motivations that represent the stance and frames prior to the election. We report that ChatGPT can predict the preferred candidate with 97\% accuracy and identify the correct voting motivation out of 13 possible choices with 71\% accuracy based on the data collected from 325 interviews. We conclude by discussing the robustness of our approach, accounting for factors such as captions quality, interview length, and channels. This new method will offer a less noisy and cost-effective alternative for opinion mining using social media data.

翻译：观点挖掘在理解公众情绪与偏好中发挥着关键作用，尤其在政治选举背景下尤为突出。传统民意调查方法虽具实用价值，但成本高昂且可扩展性有限。社交媒体为观点挖掘提供了替代性数据来源，却面临着噪声干扰、偏见偏差以及平台数据采集限制等挑战。本文提出一种新颖的观点挖掘方法，以YouTube自动生成的公开采访字幕为数据源，重点以2023年土耳其选举作为案例研究。我们构建了基于ChatGPT的观点挖掘框架，通过批量标注投票意图与动机（这些数据在选举前即体现了立场取向与叙事框架）。基于325场采访收集的数据，研究显示ChatGPT能够以97%的准确率预测候选人的支持倾向，并在13个候选选项中正确识别投票动机，准确率达71%。最后，我们从字幕质量、采访时长及频道类型等因素出发，论证了该方法的稳健性。这一新方法将为基于社交媒体数据的观点挖掘提供低噪声、高经济效益的替代方案。