# Content-Localization based System for Analyzing Sentiment and Hate Behaviors in Low-Resource Dialectal Arabic: English to Levantine and Gulf

arXiv: https://arxiv.org/abs/2312.03727 · HTML: https://mlsanity.com/paper/2312.03727/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 52 references that are in the map.
- First posted on arXiv 27 Nov 2023.

## Builds on (most cited first, 15 of 15)

- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [SemEval-2017 Task 4: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.00741/) (2019)
- [The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://mlsanity.com/paper/2103.06678/) (2021)
- [Neural Machine Translation for Low-Resource Languages: A Survey](https://mlsanity.com/paper/2106.15115/) (2021)
- [Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment Classification](https://mlsanity.com/paper/1908.11860/) (2019)
- [Detecting White Supremacist Hate Speech using Domain Specific Word Embedding with Deep Learning and BERT](https://mlsanity.com/paper/2010.00357/) (2020)
- [Let-Mi: An Arabic Levantine Twitter Dataset for Misogynistic Language](https://mlsanity.com/paper/2103.10195/) (2021)
- [Detecting Topic and Sentiment Dynamics Due to COVID-19 Pandemic Using Social Media](https://mlsanity.com/paper/2007.02304/) (2020)
- [ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets](https://mlsanity.com/paper/1906.01830/) (2019)
- [Investigating Code-Mixed Modern Standard Arabic-Egyptian to English Machine Translation](https://mlsanity.com/paper/2105.13573/) (2021)
- [A Combined CNN and LSTM Model for Arabic Sentiment Analysis](https://mlsanity.com/paper/1807.02911/) (2018)
- [Sentiment Analysis on Financial News Headlines using Training Dataset Augmentation](https://mlsanity.com/paper/1707.09448/) (2017)
- [OSN-MDAD: Machine Translation Dataset for Arabic Multi-Dialectal Conversations on Online Social Media](https://mlsanity.com/paper/2309.12137/) (2023)
- [An Online Topic Modeling Framework with Topics Automatically Labeled](https://mlsanity.com/paper/1907.01638/) (2019)

## Built on by (most cited first, 1 of 1)

- [Content-Localization based Neural Machine Translation for Informal Dialectal Arabic: Spanish/French to Levantine/Gulf Arabic](https://mlsanity.com/paper/2312.06926/) (2023)

## Abstract (arXiv)

Even though online social movements can quickly become viral on social media, languages can be a barrier to timely monitoring and analyzing the underlying online social behaviors (OSB). This is especially true for under-resourced languages on social media like dialectal Arabic; the primary language used by Arabs on social media. Therefore, it is crucial to provide solutions to efficiently exploit resources from high-resourced languages to solve language-dependent OSB analysis in under-resourced languages. This paper proposes to localize content of resources in high-resourced languages into under-resourced Arabic dialects. Content localization goes beyond content translation that converts text from one language to another; content localization adapts culture, language nuances and regional preferences from one language to a specific language/dialect. Automating understanding of the natural and familiar day-to-day expressions in different regions, is the key to achieve a wider analysis of OSB especially for smart cities. In this paper, we utilize content-localization based neural machine translation to develop sentiment and hate classifiers for two low-resourced Arabic dialects: Levantine and Gulf. Not only this but we also leverage unsupervised learning to facilitate the analysis of sentiment and hate predictions by inferring hidden topics from the corresponding data and providing coherent interpretations of those topics in their native language/dialects. The experimental evaluations and proof-of-concept COVID-19 case study on real data have validated the effectiveness of our proposed system in precisely distinguishing sentiments and accurately identifying hate content in both Levantine and Gulf Arabic dialects. Our findings shed light on the importance of considering the unique nature of dialects within the same language and ignoring the dialectal aspect would lead to misleading analysis.