# Emotion Recognition for Low-Resource Turkish: Fine-Tuning BERTurk on TREMO and Testing on Xenophobic Political Discourse

arXiv: https://arxiv.org/abs/2505.12160 · HTML: https://mlsanity.com/paper/2505.12160/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 42 references that are in the map.
- First posted on arXiv 17 May 2025.

## Builds on (most cited first, 4 of 4)

- [What the [MASK]? Making Sense of Language-Specific BERT Models](https://mlsanity.com/paper/2003.02912/) (2020)
- [How Different Text-preprocessing Techniques Using The BERT Model Affect The Gender Profiling of Authors](https://mlsanity.com/paper/2109.13890/) (2021)
- [Harnessing the Power of BERT in the Turkish Clinical Domain: Pretraining Approaches for Limited Data Scenarios](https://mlsanity.com/paper/2305.03788/) (2023)
- [Fine-tuning Transformer-based Encoder for Turkish Language Understanding Tasks](https://mlsanity.com/paper/2401.17396/) (2024)

## Built on by (most cited first, 1 of 1)

- [Benchmarking Source-Sensitive Reasoning in Turkish: Humans and LLMs under Evidential Trust Manipulation](https://mlsanity.com/paper/2604.24665/) (2026)

## Abstract (arXiv)

Social media platforms like X (formerly Twitter) play a crucial role in shaping public discourse and societal norms. This study examines the term Sessiz Istila (Silent Invasion) on Turkish social media, highlighting the rise of anti-refugee sentiment amidst the Syrian refugee influx. Using BERTurk and the TREMO dataset, we developed an advanced Emotion Recognition Model (ERM) tailored for Turkish, achieving 92.62% accuracy in categorizing emotions such as happiness, fear, anger, sadness, disgust, and surprise. By applying this model to large-scale X data, the study uncovers emotional nuances in Turkish discourse, contributing to computational social science by advancing sentiment analysis in underrepresented languages and enhancing our understanding of global digital discourse and the unique linguistic challenges of Turkish. The findings underscore the transformative potential of localized NLP tools, with our ERM model offering practical applications for real-time sentiment analysis in Turkish-language contexts. By addressing critical areas, including marketing, public relations, and crisis management, these models facilitate improved decision-making through timely and accurate sentiment tracking. This highlights the significance of advancing research that accounts for regional and linguistic nuances.