AI Wizards at CheckThat! 2025: Enhancing Transformer-Based Embeddings
  with Sentiment for Subjectivity Detection in News Articles
Authors: name: Matteo Fasulo, name: Luca Babboni, name: Luca Tedeschini
Organization: AI Wizards

This paper presents AI Wizards' participation in the CLEF 2025 CheckThat! Lab
Task 1: Subjectivity Detection in News Articles, classifying sentences as
subjective/objective in monolingual, multilingual, and zero-shot settings.
Training/development datasets were provided for Arabic, German, English,
Italian, and Bulgarian; final evaluation included additional unseen languages
(e.g., Greek, Romanian, Polish, Ukrainian) to assess generalization. Our
primary strategy enhanced transformer-based classifiers by integrating
sentiment scores, derived from an auxiliary model, with sentence
representations, aiming to improve upon standard fine-tuning. We explored this
sentiment-augmented architecture with mDeBERTaV3-base, ModernBERT-base
(English), and Llama3.2-1B. To address class imbalance, prevalent across
languages, we employed decision threshold calibration optimized on the
development set. Our experiments show sentiment feature integration
significantly boosts performance, especially subjective F1 score. This
framework led to high rankings, notably 1st for Greek (Macro F1 = 0.51).