Abstract:
Sentiment Analysis (SA) is the process of determining the sentiment of a text written in a natural
language to be positive, negative or neutral. It is one of the most interesting subfields of natural
language processing (NLP) and Web mining due to its diverse applications and the challenges
associated with applying it on the massive amounts of textual data available online (especially, on
social networks). Most of the current work on SA focus on the English language and work on the
sentence-level or the document-level. This work focuses on the less studied version of SA, which
is aspect-based SA (ABSA) for the Arabic language. Specifically, this work considers two ABSA
tasks: aspect category determination and aspect category polarity determination, and makes use of
the publicly available human annotated Arabic dataset (HAAD) along with its baseline experiments
conducted by HAAD providers. In this work, several lexicon-based approaches are presented for
the two tasks at hand and show that some of the presented approaches significantly outperforms the
best-known result on the given dataset. An enhancement of 9% and 46% were achieved in the tasks
aspect category determination and aspect category polarity determination respectively