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).

翻译：理解劳动力的偏好、观点和情感对于有效的员工生命周期管理至关重要。开放式调查回复作为一种宝贵的信息来源。本文提出了一种机器学习方法，用于对员工满意度调查中的荷兰语开放式回复进行方面级情感分析（ABSA）。我们的方法旨在克服这些回复中固有的噪声和变异性，从而实现对情感的全面分析，以支持员工生命周期管理。通过回复聚类，我们识别出六个关键方面（薪资、日程、联系、沟通、个人关注、协议），并由领域专家验证。我们构建了一个包含1,458份荷兰语调查回复的数据集，揭示了方面和情感中的标签不平衡问题。我们提出了基于荷兰语BERT模型的少样本ABSA方法，并将其与词袋和零样本基线进行了比较。本研究通过首次成功将荷兰语预训练语言模型应用于人力资源（HR）领域的方面级情感分析，对ABSA领域做出了重要贡献。