Search

Arabic is a language with rich morphology and few resources. Arabic is therefore recognized as one of the most challenging languages for machine translation. The study of translation into Arabic has received significantly less attention than that of European languages. Consequently, further research into Arabic machine translation quality needs more investigation. This paper proposes a translation model between Arabic and English based on Neural Machine Translation (NMT). The proposed model employs a transformer with multi-head attention. It combines a feed-forward network with a multi-head attention mechanism. The NMT proposed model has demonstrated its effectiveness in improving translation by achieving an impressive accuracy of 97.68%, a loss of 0.0778, and a near-perfect Bilingual Evaluation Understudy (BLEU) score of 99.95. Future work will focus on exploring more effective ways of addressing the evaluation and quality estimation of NMT for low-data resource languages, which are often challenging as a result of the scarcity of reference translations and human annotators. [ABSTRACT FROM AUTHOR]

Sentiment Analysis, also known as opinion mining, is the area of Natural Language Processing that aims to extract human perceptions, thoughts, and beliefs from unstructured textual content. It has become a useful, attractive, and challenging research area concerning the emergence and rise of social media and the mass volume of individuals' reviews, comments, and feedback. One of the major problems, apparent and evident in social media, is the toxic online textual content. People from diverse cultural backgrounds and beliefs access Internet sites, concealing and disguising their identity under a cloud of anonymity. Due to users' freedom and anonymity, as well as a lack of regulation governed by social media, cyber toxicity and bullying speech are major issues that need an automated system to be detected and prevented. There is diverse research in different languages and approaches in this area, but the lack of a comprehensive study to investigate them from all aspects is tangible. In this manuscript, a comprehensive multi-lingual and systematic review of cyber-hate sentiment analysis is presented. It states the definition, properties, and taxonomy of cyberbullying and how often each type occurs. In addition, it presents the most recent popular cyberbullying benchmark datasets in different languages, showing their number of classes (Binary/Multiple), discussing the applied algorithms, and how they were evaluated. It also provides the challenges, solutions, as well as future directions. [ABSTRACT FROM AUTHOR]

Opinion Mining (OM) has lately become one of the increasing areas of research identified with text mining and natural language processing. This domain is utilized to detect and extract the sentiment out of text giving valuable and beneficial information related to the author and his/her tendency for a precise topic. The fundamental task is to classify that extracted text which could be a tweet, review, blog, comment, news, etc. to a positive, negative, or neutral sentiment. Most of the instant investigations identified with this topic focus essentially on English textswith a limited and finite assets and resources accessible for miscellaneous languages like Arabic, and its different dialects like the Egyptian dialect, Gulf dialect and so on. This research focus on Arabic Dialects Opinion Mining (ADOM),different Machine Learning (ML) algorithms are applied and the experimental results showthat the Support Vector Machine (SVM) classifier gives the highestand most efficient accuracyof93.56% compared to other applied classifiers. Moreover, this accuracy exceeds the other Arabic related work which makes it very promising and encourages to continuein this line of researchutilizing a normalized dataset with two polarities. [ABSTRACT FROM AUTHOR]