Developing resources for sentiment analysis of informal Arabic text in social media

Natural Language Processing (NLP) applications such as text categorization, machine translation, sentiment analysis, etc., need annotated corpora and lexicons to check quality and performance. This paper describes the development of resources for sentiment analysis specifically for Arabic text in social media. A distinctive feature of the corpora and lexicons developed are that they are determined from informal Arabic that does not conform to grammatical or spelling standards. We refer to Arabic social media content of this sort as Dialectal Arabic (DA) - informal Arabic originating from and potentially mixing a range of different individual dialects. The paper describes the process adopted for developing corpora and sentiment lexicons for sentiment analysis within different social media and their resulting characteristics. The addition to providing useful NLP data sets for Dialectal Arabic the work also contributes to understanding the approach to developing corpora and lexicons

The Deceptively Simple: Translating Donald Trump’s Posts on Twitter into Arabic

This paper investigates the translation strategies that have been employed in translating Donald Trump’s posts on Twitter from English into Arabic, taking into consideration Trump’s use of language characterized by the excessive use of sentence fragments, colloquialism and a discourse that generally lacks cohesion. To achieve this goal, sixty-five tweets posted in 2019, and translated by two media news agencies, namely Russia Today and Anadolu Agency are analyzed to identify the translation strategies adopted by the translators affiliated to these agencies, and to determine the impact of the strategies utilized on the target language texts. The analysis of the corpora reveals that the three most predominant translation strategies adopted by the translators of the aforementioned media outlets are, in order of importance, explicitation, omission and shifts. Overall, these strategies produce target language texts that flow smoothly and succeed in transferring the messages originally expressed in the English tweets. However, on rare occasions when unnecessary omission and unmotivated shifts are adopted, the translators of Russia Today and Anadolu Agency fail to transmit the same communicative values Trump originally conveyed to his English-speaking followers. Keywords: Social media, Twitter, Donald Trump, Translation strategies. DOI: 10.7176/RHSS/11-22-08 Publication date: November 30th 202

Translation as a Form of Da’wah: a Unique Cultural Experience of King Fahd Glorious Qur’an Printing Complex and Its influence on Islamic Ummah

Established in 1405 A.H. (1984 CE) by His Royal Highness King Fahd bin ‘Abd al-Aziz Al Sa’ud (rahimahu Allah) Qur’an Printing Complex quickly became one of the most eminent institutions that are related not only to the propagation of Qur’anic knowledge, but to Islamic Affairs in general. The main goal of our article is to explore how the Qur’an Printing Complex obtained its highest position in Islamic world and which efforts, with the help of Allah the Almighty, were made by the wise government of Al Sa’ud and Islamic scholars from the Kingdom on the way to these achievements. It is proved that Qur’an Printing Complex completely realized Islamic view on the translation of the Glorious Qur’an and the Sunnah of Prophet Muhammad (peace be upon him) that is stated in the words of Sheikh al-Islam Ahmad ibn Taymiyah (rahimahu Allah) in his work “Refutation of Greek Logic”: “Islamic community is obliged to convey (tabligh) The Qur’an, its words and meanings… if its conveying to the foreigners needs a translation, let them [i.e. Muslims] translate it as much as possible”. We assume that King Fahd Glorious Qur’an Printing Complex is a unique religious institution in the world, since no other religion has such a powerful scientific and cultural organization that deals with sacred texts and their edition. The level of published editions, interpretations and academic requirements to the translations received the highest evaluation not only from Muslim scholars, but also from Orientalists. The well-known edition of the Glorious Mushaf (al-Mushaf al-Madinah an-Nabawiyyah), published by Complex, constitutes one of the most referred sources in the present-day Qur’anic Studies. Our article affirms that methods and other features of Islamic call, used by Qur’an Printing Complex and based on the path of as-salaf as-salih, provide a wide scientific experience that is useful for Islamic ummah in every corner of the globe

Translating Arabic as low resource language using distribution representation and neural machine translation models

University of Technology Sydney. Faculty of Engineering and Information Technology.Rapid growth in social media platforms makes the communication between users easier. According to that, the communication increased the importance of translating human languages. Machine translation technology has been widely used for translating several languages using different approaches such as rule based, statistical machine translation and more recently neural machine translation. The quality of machine translation depends on the availability of parallel datasets. Languages that lack sufficient datasets have posed many challenges related to their processing and analysis. These languages are referred to as low resource languages. In this research, we mainly focused on low resource languages, particularly Arabic and its dialects. Dialectal Arabic can be treated as non-standard text that is used in Arab social media and need to be translated to their standard forms. In this context, the importance and the focus of machine translation have been increased recently. Unlike English and other languages, translation of Arabic and its dialects have not been thoroughly investigated, where existing attempts were mostly developed based on statistic and rule-based approaches, while neural network approaches have hardly been considered. Therefore, a distribution representation model (embedding model) has been proposed to translate dialectal Arabic to Modern Standard Arabic. As Arabic is a rich morphology language that has different forms of the same words the proposed model can help to capture more linguistic features such as semantic and syntax features without any rules. Another benefit of the proposed model is that it has the capability to be trained on monolingual datasets instead of parallel datasets. This model was used to translate Egyptian dialect text to Modern Standard Arabic. We also, built a monolingual datasets from available resources and a small parallel dictionary. Different datasets were used to evaluate the performance of the proposed method. This research provides new insight into dialectal Arabic translation. Recently, there has been increased interest in Neural Machine Translation (NMT). NMT is a deep learning based model that is trained using large parallel datasets with the aim of mapping text from the source language to the target language. While it shows a promising result for high resource translation languages, such as English, low resource languages face challenges using NMT. Therefore, a number of NMT based models have been developed to translate low resource languages, for instance pre-trained models that utilize monolingual datasets. While these models were used on word level and using recurrent neural networks, which have some limitations, we proposed a hybrid model that combines recurrent and convolutional neural networks on character level to translate low resource languages

The Pleasures of Polyglossia in Emirati Cinema: Focus on From A to B and Abdullah

Polyglot films highlight the coexistence of multiple languages at the level of dialogue and narration. Even the notoriously monolingual Hollywood film industry has recently seen an increase in polyglot productions. Much of Europe's polyglot cinema reflects on postwar migration. Hamid Naficy has coined the phrase " accented cinema " to define diasporic filmmaking, a closely related category. This essay considers polyglot Emirati films as part of an increasingly popular global genre. It argues that the lack of a monolingual mandate is conducive to experiments with language choices, and that the polyglot genre serves best to emphasize efforts made to accommodate the diversity of cultures interacting in urban centers in the United Arab Emirates. Case studies of Ali F. Mostafa's From A to B (2014) and Humaid Alsuwaidi's Abdullah (2015) demonstrate the considerable contributions Emirati filmmakers have already made to a genre, which offers a powerful potential for cinema in the UAE. A comparative analysis identifies the extent to which each of the two films reveals elements inherent in three of the five sub-categories outlined by Chris Wahl