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

TL;DR

This paper develops a Turkish-specific emotion recognition model using BERTurk and TREMO, achieving high accuracy and revealing emotional patterns in social media discourse, with applications in sentiment analysis and societal understanding.

Contribution

The study introduces a fine-tuned BERTurk-based emotion recognition model for Turkish, addressing linguistic challenges and applying it to social media data for societal insights.

Findings

Achieved 92.62% accuracy in emotion classification

Uncovered emotional trends related to anti-refugee sentiment

Demonstrated practical applications in sentiment analysis

Abstract

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.