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：意见挖掘在理解公众情绪和偏好方面发挥着关键作用，特别是在政治选举的背景下。传统民意调查方法虽然有用，但成本较高且扩展性较差。社交媒体提供了一种替代的意见挖掘数据源，但存在噪声、偏见和数据收集稳定性等方面的挑战。本文提出了一种新颖的意见挖掘方法，利用YouTube公共采访自动生成的字幕作为数据源，重点关注以2023年土耳其选举为案例的研究。我们引入了一个利用ChatGPT进行投票意向和动机质量大规模注释、代表选举前的立场和框架的意见挖掘框架。我们报告了ChatGPT能够根据325个采访数据预测出 97\% 的首选候选人，并在13个可能的选择中以71\%的准确率识别出正确的投票动机。我们总结讨论了我们的方法的鲁棒性，包括字幕质量、采访长度和渠道等因素。本文提出的新方法将为使用社交媒体数据的意见挖掘提供一种更少噪声且成本效益高的替代方案。
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