This paper scrutinizes a database of over 4900 YouTube videos to characterize financial market coverage. Financial market coverage generates a large number of videos. Therefore, watching these videos to derive actionable insights could be challenging and complex. In this paper, we leverage Whisper, a speech-to-text model from OpenAI, to generate a text corpus of market coverage videos from Bloomberg and Yahoo Finance. We employ natural language processing to extract insights regarding language use from the market coverage. Moreover, we examine the prominent presence of trending topics and their evolution over time, and the impacts that some individuals and organizations have on the financial market. Our characterization highlights the dynamics of the financial market coverage and provides valuable insights reflecting broad discussions regarding recent financial events and the world economy.
翻译：本文审视了4900多个YouTube视频数据库,以描述金融市场的覆盖范围。金融市场覆盖面生成了大量视频。因此,观看这些视频以获得可操作的洞察力可能具有挑战性和复杂性。在本文中,我们利用OpenAI的语音对文本模型Whisper生成了布隆伯格和雅虎金融公司的市场覆盖面视频文本。我们利用自然语言处理从市场覆盖面中获取语言使用方面的见解。此外,我们审视了趋势性议题的显著存在及其随时间演变,以及一些个人和组织对金融市场的影响。我们的特征特征突出显示了金融市场覆盖面的动态,提供了反映近期金融事件和世界经济广泛讨论的宝贵见解。
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