  Understanding preferences, opinions, and sentiment of the workforce is
paramount for effective employee lifecycle management. Open-ended survey
responses serve as a valuable source of information. This paper proposes a
machine learning approach for aspect-based sentiment analysis (ABSA) of Dutch
open-ended responses in employee satisfaction surveys. Our approach aims to
overcome the inherent noise and variability in these responses, enabling a
comprehensive analysis of sentiments that can support employee lifecycle
management. Through response clustering we identify six key aspects (salary,
schedule, contact, communication, personal attention, agreements), which we
validate by domain experts. We compile a dataset of 1,458 Dutch survey
responses, revealing label imbalance in aspects and sentiments. We propose
few-shot approaches for ABSA based on Dutch BERT models, and compare them
against bag-of-words and zero-shot baselines. Our work significantly
contributes to the field of ABSA by demonstrating the first successful
application of Dutch pre-trained language models to aspect-based sentiment
analysis in the domain of human resources (HR).
