# Dhati+: Fine-tuned Large Language Models for Arabic Subjectivity Evaluation

arXiv: https://arxiv.org/abs/2508.19966 · HTML: https://mlsanity.com/paper/2508.19966/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 55 references that are in the map.
- First posted on arXiv 27 Aug 2025.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [AraBERT: Transformer-based Model for Arabic Language Understanding](https://mlsanity.com/paper/2003.00104/) (2020)
- [SemEval-2017 Task 4: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.00741/) (2019)
- [LABR: A Large Scale Arabic Sentiment Analysis Benchmark](https://mlsanity.com/paper/1411.6718/) (2014)
- [Unsupervised Aspect Term Extraction with B-LSTM & CRF using Automatically Labelled Datasets](https://mlsanity.com/paper/1709.05094/) (2017)
- [A review of sentiment analysis research in Arabic language](https://mlsanity.com/paper/2005.12240/) (2020)
- [ArabianGPT: Native Arabic GPT-based Large Language Model](https://mlsanity.com/paper/2402.15313/) (2024)
- [Polarity and Subjectivity Detection with Multitask Learning and BERT Embedding](https://mlsanity.com/paper/2201.05363/) (2022)
- [A Semi-supervised Approach for a Better Translation of Sentiment in Dialectical Arabic UGT](https://mlsanity.com/paper/2210.11899/) (2022)

## Abstract (arXiv)

Despite its significance, Arabic, a linguistically rich and morphologically complex language, faces the challenge of being under-resourced. The scarcity of large annotated datasets hampers the development of accurate tools for subjectivity analysis in Arabic. Recent advances in deep learning and Transformers have proven highly effective for text classification in English and French. This paper proposes a new approach for subjectivity assessment in Arabic textual data. To address the dearth of specialized annotated datasets, we developed a comprehensive dataset, AraDhati+, by leveraging existing Arabic datasets and collections (ASTD, LABR, HARD, and SANAD). Subsequently, we fine-tuned state-of-the-art Arabic language models (XLM-RoBERTa, AraBERT, and ArabianGPT) on AraDhati+ for effective subjectivity classification. Furthermore, we experimented with an ensemble decision approach to harness the strengths of individual models. Our approach achieves a remarkable accuracy of 97.79,% for Arabic subjectivity classification. Results demonstrate the effectiveness of the proposed approach in addressing the challenges posed by limited resources in Arabic language processing.