Abstract:
Aspect Based Sentiment Analysis (ABSA) also known as entity level analysis has gained
the vital importance to extract the people’s sentiment, emotions or opinions towards some
entity. This entity can be any organization, social platforms etc. In spite of the fact that
Sentiment Analysis (SA) has been examined broadly within the English language domain
with some eminent work in other dialects including Chinese, Arabic etc., many other
resource poor languages including Urdu did not get much attention of researchers due to
the lack of resources. Urdu is widely spoken language in all over the world and various
social platforms are full of Urdu reviews containing people’s sentiments. In this study we
have established an aspect level rule-based approach for the Urdu language. To perform
our work, we have used the Urdu dialect dataset containing the “COVID-19” tweets. These
tweets contain all the information related to Coronavirus and people’s views towards this
disease. Sentiment lexicon has been used to extract for the opinion term present in a tweet
and after getting the opinion term various aspect have been extracted associated with the
opinion. Aspect extraction has been performed using various rule, these rules have been
created using linguistic and syntactic context of the phrases present in the tweet and
polarities are assigned accordingly. The proposed study focused to achieve four crucial
modules: aspect term, aspect term polarity, aspect term category and aspect term category
polarity. With the help of evaluation measures including F1-Score, Accuracy, Precision
and Recall our work has achieved the promising results.
