I am a datascience PhD student conducting research in Sentiment Analysis for dialectal languages on social media with a specific focus on Arabic. Unlike other languages, Arabic is very dialectal and differs significantly among regions. Many Arabs tend to express their natural mother-tongue in text on social media without following a unified orthography. This unstructured form of language lacks semantic and linguistic resources, making it a challenge for the Arabic Natural Language Processing. Through my research I plan to leverage sentiment analysis methods to handle the heterogeneous Arabic social data. Extracting sentiment from text stems from my interest in Affective Computing: Developing machines that can understand, and react to, human emotion.

Keys: Data Science, Natural Language Processing, Sentiment Analysis.

Team: Harith Alani, Miriam Fernandez, Chris Sanders

Tobaili, T., Fernandez, M., Alani, H., Sharafeddine, S., Hajj, H. and Glavas, G. (2019) SenZi: A Sentiment Analysis Lexicon for the Latinised Arabic (Arabizi), RANLP 2019, Varna, Bulgaria

Tobaili, T. (2016) Arabizi Identification in Twitter Data, Workshop: SRW at ACL 2016