Arabic Sentiment Analysis in the Ar-PHP library

Dear Zaid and ARBML team,

Thanks for your initiative to support Arabic people who are working on NLP and need guidance or would like to brainstorm.

Well, let me introduce myself, my name is Khaled Al-Shamaa, and I am working on the [PHP and Arabic language](https://github.com/khaled-alshamaa/ar-php) library as a side project since 2006. This library provides a set of tools that enable Arabic website developers to provide a professional search, present, and process Arabic content in PHP.

Recently we put a goal for 2021 to introduce few Arabic NLP functionalities to our library, and we started with the [Arabic sentiment analysis](https://ar-php.org/github/examples/ar_sentiment.php). But we followed a bit different approach (rather than BERT models like your [Arabic mobileBERT](https://arbml.github.io/ARBML/Interfaces/Website/ArabicSentimentClassificationBert/index.html)) seeking simplicity and the ability to work using minimum resources (our model is less than 30Kb in size and deployed perfectly fine even in interpreted languages like PHP or JavaScript).

We used a pragmatic approach by accepting that all the words in the first language spoken by the Semitic peoples consisted of bi-radicals (i.e., two sounds/letters). Therefore, we can handle the majority of Arabic word roots as being expanded by the addition of a third letter, with the resulting meaning having a semantic relation to the original bi-radical.

We built a statistical log-odds scores model to tag the tone (positive, negative) of each word (actually two letters root for that word) using a dataset published on [Kaggle](https://www.kaggle.com/abedkhooli/arabic-100k-reviews) that includes 100k Arabic reviews from hotels, books, movies, products, and a few airlines.

We would like to get an external review for our model accuracy and performance by your team to see how decent this model is. It will be a great opportunity if we can collaborate to bring more open-source NLP tools into the hands of Arabic web developers.

Best regards,
Khaled

Start with the linked Arabic sentiment example at ar-php.org/github/examples/ar_sentiment.php and compare its stated approach with the linked Arabic mobileBERT interface. Review the model's accuracy and performance using the described 100k Arabic reviews dataset, then document whether the model is suitable for collaboration and further NLP tooling.

Written by the indexing model from the issue text.