Does Translation Preserve Sentiment? An Analysis of Arabic-English Cross-Lingual Classification
Year: 2026
Type: conference-paper
Abstract: Machine translation is widely used in cross-lingual sentiment analysis, yet the assumption that translation preserves sentiment remains largely unexamined.We present a systematic analysis of translation-induced sentiment shifts across 11,558 samples from three Arabic-English datasets (AJGT, OCLAR, FSA) using three translation models (Helsinki-NMT, GPT-4o-mini, LLaMA-3.1-8B) and a fixed multilingual classifier (XLM-RoBERTa).A substantial proportion of samples experience sentiment shifts after translation, with accuracy drops ranging from less than 1% to nearly 20%.GPT-4o-mini achieves the strongest sentiment preservation, while LLaMA-3.1-8Bexhibits both significant distortion and refusal behaviour.Critically, Helsinki-NMT's successful translation of all samples indicates that LLaMA's refusals stem from safety policies rather than input untranslatability.We also find that sentiment shift measurements are pipeline-dependent and vary with the classifier used for evaluation.These findings challenge the translate-then-classify paradigm and provide guidance for cross-lingual Arabic NLP systems.
Authors: Nour Aldin Al Mubarak, Noura Al Moubayed
Institution: Durham University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166570361