Aspect-based sentiment analysis in Urdu language: resource creation and evaluation

With the advancement in web interactions and increased use of Online Social Networks, sentiment analysis has gained popularity. Topics like sports, health, music, and technology are widely debated on in OSN, especially on twitter. People share their activities, views, and feelings toward different events in their native languages that can be analyzed using sentiment analysis to understand the sentiments of the people toward these events. For English language, studies on sentiment analysis are vastly available. However, very little work exists on sentiment analysis for resource-scarce language like Urdu. For this study, we perform aspect-based sentiment analysis on sports tweets in Urdu language by extracting the following information from a sentence, i.e., aspect terms, aspect term polarity, aspect category, and aspect category polarity, using machine learning and deep learning classifiers. This work is the first effort in aspect-based sentiment analysis for Urdu language using classical machine learning and deep learning approach. Additionally, we also identify implicit aspects from a sentence. Our proposed approach shows classical machine learning approach performed better on the tasks of aspect term polarity, aspect category, and aspect category polarity, while deep learning model outperformed classical machine learning classifiers for the task of aspect term/s.