Happiness at Work is considered the Holy Grail of organizational sciences. The belief that happier workers are more productive leads to a win-win situation for both individuals and organizations. Nevertheless, years of research have not brought a convergent conclusion about the topic, mainly due to the lack of a widely accepted measure. Usually, questionnaires and self-report surveys are used; however, these methods embed shortcomings that allow studies’ results to be questioned. In order to overcome these shortcomings, the present study proposes a different approach to measure Happiness at Work, bringing mixed methods to encompass the complexity of the phenomenon. Based on work-life narratives and following Kahneman’s concepts, the proposed approach puts together Narrative Analysis and Sentiment Analysis. Although increasingly used to assess social media reviews, Sentiment Analysis is not yet applied to narratives related to Happiness at Work. Four methods to calculate the Happy Level indicator were tested on actual research data: one manual, through traditional coding processes, and three automatic methods to provide scalability. An example of the Happy Level application is also provided to illustrate how the indicator could improve analyses. The present study concludes that despite the manual method presents better results at this moment; the automatic ones are promising. The results also indicate paths for improvement of these methods.

工作中的幸福被认为是组织科学的圣杯。认为快乐的工人更有生产力的信念导致个人和组织双赢。然而，由于缺乏广泛接受的措施，多年的研究尚未就该主题得出一致的结论。通常，使用问卷调查和自我报告调查；但是，这些方法存在缺点，使人们对研究结果提出质疑。为了克服这些缺点，本研究提出了一种不同的方法来衡量工作中的幸福感，提出了混合方法来涵盖现象的复杂性。基于工作生活的叙述并遵循卡尼曼的概念，所提出的方法将叙事分析和情感分析结合在一起。尽管越来越多地使用情感分析来评估社交媒体评论，但情感分析尚未应用于与工作中的幸福相关的叙述中。在实际研究数据上测试了四种计算“幸福度”指标的方法：一种是通过传统编码过程进行的手动操作，另一种是提供可伸缩性的自动方法。还提供了“幸福水平”应用程序的示例，以说明指标如何改进分析。本研究得出的结论是，尽管目前采用手动方法，但效果更好。自动的是有前途的。结果还表明了这些方法的改进途径。通过传统的编码过程以及三种自动方法来提供可伸缩性。还提供了“幸福水平”应用程序的示例，以说明指标如何改进分析。本研究得出的结论是，尽管目前采用手动方法，但效果更好。自动的是有前途的。结果还指出了改进这些方法的途径。通过传统的编码过程以及三种自动方法来提供可伸缩性。还提供了“幸福水平”应用程序的示例，以说明指标如何改进分析。本研究的结论是，尽管目前采用手动方法，但效果更好。自动的是有前途的。结果还表明了这些方法的改进途径。