# Is it Great or Terrible? Preserving Sentiment in Neural Machine Translation of Arabic Reviews

arXiv: https://arxiv.org/abs/2010.13814 · HTML: https://mlsanity.com/paper/2010.13814/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2020.

## Built on by (most cited first, 10 of 10)

- [BLEU, METEOR, BERTScore: Evaluation of Metrics Performance in Assessing Critical Translation Errors in Sentiment-oriented Text](https://mlsanity.com/paper/2109.14250/) (2021)
- [A Survey of Code-switched Arabic NLP: Progress, Challenges, and Future Directions](https://mlsanity.com/paper/2501.13419/) (2025)
- [Challenges in Translation of Emotions in Multilingual User-Generated Content: Twitter as a Case Study](https://mlsanity.com/paper/2106.10719/) (2021)
- [Sentiment-Aware Measure (SAM) for Evaluating Sentiment Transfer by Machine Translation Systems](https://mlsanity.com/paper/2109.14895/) (2021)
- [A Semi-supervised Approach for a Better Translation of Sentiment in Dialectical Arabic UGT](https://mlsanity.com/paper/2210.11899/) (2022)
- [Applications and Challenges of Sentiment Analysis in Real-life Scenarios](https://mlsanity.com/paper/2301.09912/) (2023)
- [AHaSIS: Shared Task on Sentiment Analysis for Arabic Dialects](https://mlsanity.com/paper/2511.13335/) (2025)
- [Sentiment Analysis in Digital Spaces: An Overview of Reviews](https://mlsanity.com/paper/2310.19687/) (2023)
- [Cyber Risks of Machine Translation Critical Errors : Arabic Mental Health Tweets as a Case Study](https://mlsanity.com/paper/2405.11668/) (2024)
- [Is Textual Similarity Invariant under Machine Translation? Evidence Based on the Political Manifesto Corpus](https://mlsanity.com/paper/2605.00618/) (2026)

## Abstract (arXiv)

Since the advent of Neural Machine Translation (NMT) approaches there has been a tremendous improvement in the quality of automatic translation. However, NMT output still lacks accuracy in some low-resource languages and sometimes makes major errors that need extensive post-editing. This is particularly noticeable with texts that do not follow common lexico-grammatical standards, such as user generated content (UGC). In this paper we investigate the challenges involved in translating book reviews from Arabic into English, with particular focus on the errors that lead to incorrect translation of sentiment polarity. Our study points to the special characteristics of Arabic UGC, examines the sentiment transfer errors made by Google Translate of Arabic UGC to English, analyzes why the problem occurs, and proposes an error typology specific of the translation of Arabic UGC. Our analysis shows that the output of online translation tools of Arabic UGC can either fail to transfer the sentiment at all by producing a neutral target text, or completely flips the sentiment polarity of the target word or phrase and hence delivers a wrong affect message. We address this problem by fine-tuning an NMT model with respect to sentiment polarity showing that this approach can significantly help with correcting sentiment errors detected in the online translation of Arabic UGC.