Warwick Business School, University of Warwick, Coventry, United Kingdom.
Department of Computer Science, University of Cyprus, Nicosia, Cyprus.
PLoS One. 2021 Jul 30;16(7):e0254337. doi: 10.1371/journal.pone.0254337. eCollection 2021.
Sentiment analysis is an evolving field of study that employs artificial intelligence techniques to identify the emotions and opinions expressed in a given text. Applying sentiment analysis to study the billions of messages that circulate in popular online social media platforms has raised numerous opportunities for exploring the emotional expressions of their users. In this paper we combine sentiment analysis with natural language processing and topic analysis techniques and conduct two different studies to examine whether engagement in entrepreneurship is associated with more positive emotions expressed on Twitter. In study 1, we investigate three samples with 6.717.308, 13.253.244, and 62.067.509 tweets respectively. We find that entrepreneurs express more positive emotions than non-entrepreneurs for most topics. We also find that social entrepreneurs express more positive emotions, and that serial entrepreneurs express less positive emotions than other entrepreneurs. In study 2, we use 21.491.962 tweets to explore 37.225 job-status changes by individuals who entered or quit entrepreneurship. We find that a job change to entrepreneurship is associated with a shift in the expression of emotions to more positive ones.
情感分析是一个不断发展的研究领域，它运用人工智能技术来识别给定文本中表达的情感和意见。将情感分析应用于研究在流行的在线社交媒体平台上传播的数十亿条信息，为探索用户的情感表达提供了许多机会。在本文中，我们结合情感分析、自然语言处理和主题分析技术，进行了两项不同的研究，以检验在 Twitter 上从事创业活动是否与表达更积极的情绪有关。在研究 1 中，我们调查了三个样本，分别包含 6717308、13253244 和 62067509 条推文。我们发现，对于大多数主题，企业家比非企业家表达更积极的情绪。我们还发现，社会企业家比其他企业家表达更积极的情绪，而连续创业者则表达的积极情绪较少。在研究 2 中，我们使用了 21491962 条推文，来探索 37225 名个人的工作状态变化，这些人进入或退出了创业活动。我们发现，从一份工作转换到创业工作与情绪表达变得更加积极有关。
