# Aspect-Based Sentiment Analysis for Open-Ended HR Survey Responses

arXiv: https://arxiv.org/abs/2402.04812 · HTML: https://mlsanity.com/paper/2402.04812/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 41 references that are in the map.
- First posted on arXiv 7 Feb 2024.

## Builds on (most cited first, 9 of 9)

- [Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach](https://mlsanity.com/paper/1909.00161/) (2019)
- [BERTje: A Dutch BERT Model](https://mlsanity.com/paper/1912.09582/) (2019)
- [RobBERT: a Dutch RoBERTa-based Language Model](https://mlsanity.com/paper/2001.06286/) (2020)
- [Interpreting and Evaluating Neural Network Robustness](https://mlsanity.com/paper/1905.04270/) (2019)
- [vONTSS: vMF based semi-supervised neural topic modeling with optimal transport](https://mlsanity.com/paper/2307.01226/) (2023)
- [Transformers are Short Text Classifiers: A Study of Inductive Short Text Classifiers on Benchmarks and Real-world Datasets](https://mlsanity.com/paper/2211.16878/) (2022)
- [Multi-Label Few-Shot Learning for Aspect Category Detection](https://mlsanity.com/paper/2105.14174/) (2021)
- [A Joint Learning Approach for Semi-supervised Neural Topic Modeling](https://mlsanity.com/paper/2204.03208/) (2022)
- [UU-Tax at SemEval-2022 Task 3: Improving the generalizability of language models for taxonomy classification through data augmentation](https://mlsanity.com/paper/2210.03378/) (2022)

## Built on by (most cited first, 3 of 3)

- [Natural Language Processing for Human Resources: A Survey](https://mlsanity.com/paper/2410.16498/) (2024)
- [Questionnaire meets LLM: A Benchmark and Empirical Study of Structural Skills for Understanding Questions and Responses](https://mlsanity.com/paper/2510.26238/) (2025)
- [Exploring Zero-Shot ACSA with Unified Meaning Representation in Chain-of-Thought Prompting](https://mlsanity.com/paper/2512.19651/) (2025)

## Abstract (arXiv)

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).