# LLMs vs. Traditional Sentiment Tools in Psychology: An Evaluation on Belgian-Dutch Narratives

arXiv: https://arxiv.org/abs/2511.07641 · HTML: https://mlsanity.com/paper/2511.07641/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 10 Nov 2025.

## Builds on (most cited first, 7 of 7)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Identifying and Reducing Gender Bias in Word-Level Language Models](https://mlsanity.com/paper/1904.03035/) (2019)
- [Emotion Detection for Misinformation: A Review](https://mlsanity.com/paper/2311.00671/) (2023)
- [Massively Multilingual Corpus of Sentiment Datasets and Multi-faceted Sentiment Classification Benchmark](https://mlsanity.com/paper/2306.07902/) (2023)
- [ChocoLlama: Lessons Learned From Teaching Llamas Dutch](https://mlsanity.com/paper/2412.07633/) (2024)
- [SentimentArcs: A Novel Method for Self-Supervised Sentiment Analysis of Time Series Shows SOTA Transformers Can Struggle Finding Narrative Arcs](https://mlsanity.com/paper/2110.09454/) (2021)
- [GEITje 7B Ultra: A Conversational Model for Dutch](https://mlsanity.com/paper/2412.04092/) (2024)

## Abstract (arXiv)

Understanding emotional nuances in everyday language is crucial for computational linguistics and emotion research. While traditional lexicon-based tools like LIWC and Pattern have served as foundational instruments, Large Language Models (LLMs) promise enhanced context understanding. We evaluated three Dutch-specific LLMs (ChocoLlama-8B-Instruct, Reynaerde-7B-chat, and GEITje-7B-ultra) against LIWC and Pattern for valence prediction in Flemish, a low-resource language variant. Our dataset comprised approximately 25000 spontaneous textual responses from 102 Dutch-speaking participants, each providing narratives about their current experiences with self-assessed valence ratings (-50 to +50). Surprisingly, despite architectural advancements, the Dutch-tuned LLMs underperformed compared to traditional methods, with Pattern showing superior performance. These findings challenge assumptions about LLM superiority in sentiment analysis tasks and highlight the complexity of capturing emotional valence in spontaneous, real-world narratives. Our results underscore the need for developing culturally and linguistically tailored evaluation frameworks for low-resource language variants, while questioning whether current LLM fine-tuning approaches adequately address the nuanced emotional expressions found in everyday language use.