SSA-SDA: Subjectivity and Sentiment Analysis of Sudanese Dialect Arabic

In the past few years, the Subjectivity and Sentiment Analysis (SSA) has garnered a lot of attention, and many unsuccessful attempts were made for developing an SSA model for the morphologically-rich languages like Arabic. In this study, we aimed to fill the gap by designing a new manual and auto-annotated corpus of the Sudanese-Dialect Arabic (SDA) along with a novel polarity lexicon. The corpus is a collection of political tweets, which were annotated by Twitter on a sentence-level. We have also described some automated, online SSA-tagging tools, which could explore all annotated data. Furthermore, we have investigated the effect of various pre-processing techniques on the Sudanese SSA process. Here, we presented a novel SSA technique for the Sudanese dialect, which displayed a good subjectivity classification result and showed an 83.5% accuracy for the Decision Tree performance.