AI-Driven Sentiment Analysis Technologies at Work: Theorising Algorithmic Emotional Labour
Bibliographic record
Abstract
The introduction of AI-based monitoring systems in organisations is not only reshaping the employee-customer relationship, but also changing the way colleagues interact with each other. Specifically, in response to facial, voice and/or text-based emotion recognition systems, employees must perform additional emotional labour to ensure that they are perceived as positive, high-performing individuals. Based on workshops in which individuals could safely experiment with such systems, and on follow-up interviews (group and individual), we theorise a new form of emotional labour, which we call Algorithmic Emotional Labour (AEL). We define AEL as a set of practices triggered by the need to decipher the algorithm’s evaluative criteria and to ‘speak’ the algorithm’s language. These practices amount to a ‘double’ form of emotional labour, directed at interlocutors and at the algorithm itself. While AEL builds on the foundational concepts of emotional labour, such as the management of emotions to present a desired emotional state in response to specific display rules, we show that these rules are now shaped by the requirements of the algorithm, rather than solely by social or organisational norms. This introduces additional layers of regulation, strategies, and effort by individuals who must continually regulate their emotional expression in ways that respond to the unpredictable criteria set by algorithms and compensate for their limitations, such as their inability to understand nuanced or context-specific emotional cues.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".