AI Wizards at CheckThat! 2025: Enhancing Transformer-Based Embeddings
  with Sentiment for Subjectivity Detection in News Articles
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).
6878a0ee001546c83aa4fa25
Matteo Fasulo
claimed_verified
2025-07-17T08:17:28.898Z
65c4aa3f08a42345687aaa3a
https://cdn-avatars.huggingface.co/v1/production/uploads/65c4aa3f08a42345687aaa3a/8HZT84z58rp6YMRyX_zha.jpeg
MatteoFasulo
user
MatteoFasulo
6878a0ee001546c83aa4fa26
Luca Babboni
claimed_verified
2025-07-17T08:17:26.160Z
64cf700c749587dbe0172d39
https://cdn-avatars.huggingface.co/v1/production/uploads/64cf700c749587dbe0172d39/VoZYUTI9SwfSCZJD64UtH.jpeg
Luca
user
ElectroDuck
6878a0ee001546c83aa4fa27
Luca Tedeschini
claimed_verified
2025-07-17T08:17:31.749Z
647e3bab11084fb5831c4c51
/avatars/8126f7406f1516cbedfaff16b2617c6e.svg
Luca Tedeschini 
user
Luca289
AI Wizards at CheckThat! 2025: Enhancing Transformer-Based Embeddings
  with Sentiment for Subjectivity Detection in News Articles
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).
Sentiment-augmented transformer-based classifiers improve subjectivity detection in multilingual and zero-shot settings, achieving high performance and ranking first for Greek.
transformer-based classifiers sentiment scores mDeBERTaV3-base ModernBERT-base Llama3.2-1B decision threshold calibration class imbalance subjective F1 score Macro F1