Thinking Aloud or Screaming Inside: Exploratory Study of Sentiment Around Work

Abstract

Background: Millions of workers experience work-related ill health every year. The loss of working days often accounts for poor well-being because of discomfort and stress caused by the workplace. The ongoing pandemic and postpandemic shift in socioeconomic and work culture can continue to contribute to adverse work-related sentiments. Critically investigating state-of-the-art technologies, this study identifies the research gaps in recognizing workers' need for well-being support, and we aspire to understand how such evidence can be collected to transform the workforce and workplace.

Objective: Building on recent advances in sentiment analysis, this study aims to closely examine the potential of social media as a tool to assess workers' emotions toward the workplace.

Methods: This study collected a large Twitter data set comprising both pandemic and prepandemic tweets facilitated through a human-in-the-loop approach in combination with unsupervised learning and meta-heuristic optimization algorithms. The raw data preprocessed through natural language processing techniques were assessed using a generative statistical model and a lexicon-assisted rule-based model, mapping lexical features to emotion intensities. This study also assigned human annotations and performed work-related sentiment analysis.

Results: A mixed methods approach, including topic modeling using latent Dirichlet allocation, identified the top topics from the corpus to understand how Twitter users engage with discussions on work-related sentiments. The sorted aspects were portrayed through overlapped clusters and low intertopic distances. However, further analysis comprising the Valence Aware Dictionary for Sentiment Reasoner suggested a smaller number of negative polarities among diverse subjects. By contrast, the human-annotated data set created for this study contained more negative sentiments. In this study, sentimental juxtaposition revealed through the labeled data set was supported by the n-gram analysis as well.

Conclusions: The developed data set demonstrates that work-related sentiments are projected onto social media, which offers an opportunity to better support workers. The infrastructure of the workplace, the nature of the work, the culture within the industry and the particular organization, employers, colleagues, person-specific habits, and upbringing all play a part in the health and well-being of any working adult who contributes to the productivity of the organization. Therefore, understanding the origin and influence of the complex underlying factors both qualitatively and quantitatively can inform the next generation of workplaces to drive positive change by relying on empirically grounded evidence. Therefore, this study outlines a comprehensive approach to capture deeper insights into work-related health.

Keywords: Bayesian inference; artificial intelligence; machine learning; mobile phone; natural language processing; occupational health; sentiment analysis; work-related mental health.

Figure 1

Preprocessing and analysis steps for both labeled and unlabeled data. LDA: latent Dirichlet allocation; VADER: Valence Aware Dictionary for Sentiment Reasoner.

Figure 2

Distribution of the optimal number of topics with the top 30 salient words. PC1: principal component 1; PC2: principal component 2.

Figure 3

Top words in each topic within the unlabeled data set using latent Dirichlet allocation Mallet.

Figure 4

Sentiment intensity of the unlabeled tweets using Valence Aware Dictionary for Sentiment Reasoner.

Figure 5

The proportion of positive, negative, and neutral unlabeled tweets using Valence Aware Dictionary for Sentiment Reasoner.

Figure 6

Dynamic relationship between work life and health.

Figure 7

High-level architecture of artificial intelligence–enabled mental health support for the workforce.

Figure 8

Self-imposed digital surveillance without a security breach.