Aspect Based Sentiment Analysis for Urdu Language Using Rule-Based Approac

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.