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.

Clustering Algorithms in Sentiment Analysis Techniques in Social Media – A Rapid Literature Review

“…There is proof that being exposed to weather hazards has a negative influence on people's physical and mental health, especially in places affected by heat islands and climate change. The study in [71] reveals how Twitter data may be used to measure urban heat stress in real time and serve as a quick signal of times when people are feeling more uncomfortable due to the heat and are more likely to be dissatisfied with the weather.…”

“…Moreover, the inhabitants of a region may presently convey their own experiences with warm weather as well as their sentiments about it on SM. The public mood and health of an area may be reflected in the geotagged, time-stamped, and easily available SM databases, according to a recent study published by [71]. Further, research on the emotional data may be done using the Roulette wheel selection based cuckoo search clustering method [19].…”

Relationship between duration of sick leave and time variation of words used in return-to-work programs for depression

“…There have been several reports of NLP studies targeting workers. For example, a study investigating work-related tweets using Twitter reported that tweets containing words such as “life” and “need” were included regardless of the emotion expressed in the tweet [ 25 ]. Another study that used NLP to investigate work-related suicides in the National Violent Death Reporting System database identified factors such as mental health issues and conflicts in relationships as preceding factors of suicide [ 26 ].…”