Abstract:
An important change in the dynamics of employment has emerged in Sri
Lanka due to the economic downturn. Remote work has become a hot topic
due to the on-going crisis. This study examines public attitudes toward
remote work in Sri Lanka amid the current economic crisis. With the rise of
remote work due to employment uncertainties, the research seeks to
understand how people view remote jobs in comparison to traditional roles.
Utilizing Twitter as the main source of data, the study employed Natural
Language Processing (NLP) techniques and Machine Learning (ML)
algorithms, including Naïve Bayes, Support Vector Machine (SVM),
Random Forest, Logistic Regression, and Gradient Boosting, to categorize
tweets from 2020 to 2024 into positive, negative, and neutral sentiments. The
results indicated that SVM was the most successful model, achieving an 85%
accuracy rate according to precision, recall, and F1 score evaluations. The
sentiment analysis showed 41% negative, 30% positive, and 29% neutral
sentiments, demonstrating the range of public viewpoints on remote work
during this crisis. While these insights are valuable, the demographic
limitations of Twitter, which skews toward younger, tech-savvy users, could
restrict the broader applicability of the results. Future studies should
investigate using other data sources, such as surveys, to gain a wider
understanding. The research underscores the significance of hybrid work
models and stresses the urgent need for enhanced digital infrastructure and
support systems to facilitate remote work in the challenging economic
landscape of Sri Lanka. Finally, the study provides valuable insights for
policymakers and employers concerning the evolving job scene in Sri Lanka
and emphasizes the need for improved digital infrastructure and support
systems for remote work.
