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Thinking Aloud or Screaming Inside: Exploratory Study of Sentiment Around Work
Marzia Hoque Tania1, Md Razon Hossain2, Nuzhat Jahanara3
1Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom.
JMIR Formative Research
|September 30, 2022
Summary
Social media data reveals workers' sentiments about their jobs, offering insights to improve workplace well-being. This study used advanced analysis to understand employee emotions and support positive change.
Area of Science:
- Computational Social Science
- Natural Language Processing
- Occupational Health Psychology
Background:
- Millions of workers suffer from work-related ill health annually, impacting well-being due to workplace discomfort and stress.
- The post-pandemic shift in work culture exacerbates adverse work-related sentiments, highlighting the need for better support systems.
- Existing technologies require critical investigation to identify research gaps in recognizing and addressing workers' well-being needs.
Purpose of the Study:
- To examine the potential of social media, specifically Twitter, as a tool for assessing workers' emotions toward their workplace.
- To leverage advances in sentiment analysis to understand employee sentiments and inform workplace transformations.
- To bridge the gap between technological capabilities and the collection of empirical evidence for improving worker well-being.
Main Methods:
- Collected a large dataset of Twitter data (pandemic and prepandemic) using a human-in-the-loop approach.
- Employed unsupervised learning and meta-heuristic optimization algorithms for data analysis.
- Utilized natural language processing (NLP), generative statistical models, lexicon-assisted rule-based models, and human annotations for sentiment analysis.
Main Results:
- Topic modeling (Latent Dirichlet Allocation) identified key themes in discussions on work-related sentiments on Twitter.
- Sentiment analysis revealed a complex interplay of emotions, with human-annotated data showing a higher proportion of negative sentiments.
- N-gram analysis supported the findings from sentimental juxtaposition in the labeled dataset, highlighting specific linguistic patterns.
Conclusions:
- Work-related sentiments are significantly reflected on social media, providing a valuable resource for understanding and supporting workers.
- Factors influencing worker health and well-being are multifaceted, including workplace infrastructure, job nature, organizational culture, and personal elements.
- A comprehensive, empirically grounded approach is essential for driving positive change in future workplaces by capturing deeper insights into work-related health.
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