Algorithmic Management and Gig-Worker Sentiment over Time: A Comparative Analysis of Literature and Computational Evidence

Algorithmic management increasingly shapes how gig work is organised, evaluated, and experienced, yet its implications for workers’ affective responses remain fragmented across the literature. This study integrates a systematic review with computational analysis to examine how algorithmically mediated work is represented in research and expressed in online gig-worker discourse. The systematic review followed PRISMA guidelines and synthesised evidence on autonomy, fairness, transparency, evaluation, resource insecurity, and worker experience. The computational component analysed a corpus of 10,000 online texts collected over 12 consecutive months in 2025 from gig-worker-related digital communities and platforms. Sentiment and emotion were examined using NLP-based classification and term extraction, with temporal and contextual patterns assessed across the corpus. The computational analysis found that negative sentiment was more prevalent than neutral and positive sentiment, with frustration, anxiety, and anger among the most prominent expressed emotions. These patterns broadly corresponded with themes identified in the systematic review, particularly concerns regarding fairness, uncertainty, autonomy, transparency, and algorithmic control. However, the temporal findings represent changes in aggregate online discourse rather than within-person emotional trajectories or causal effects. The study concludes that computational analysis provides complementary evidence of how algorithmically mediated work is discussed and affectively expressed online, while individual lived experiences, psychological outcomes, and causal mechanisms require further investigation through longitudinal and mixed-method research.