Urdu Sentiment Analysis Using Deep Attention-based Technique

Abstract

Sentiment analysis (SA) is a process that aims to classify text into positive, negative, or neutral categories. It has recently gained the research community's attention because of the abundance of opinion data to be processed for better understanding and decision-making. Deep learning techniques have recently shown tremendous performance, with a high tendency to reveal the underlying semantic meaning of text inputs. Since deep learning techniques are seen as black boxes, their effectiveness comes in the form of interpretability. The major goal of this article is to create an Urdu SA model that can comprehend review semantics without the need for language resources. The proposed model is tested on reviews to extract significant words using various scenarios and architectures. By emphasizing the most informative terms to the class label, the results demonstrated the suggested model's capacity to interpret a given review. Furthermore, the suggested models provide a visualization option for an intelligible explanation of the result. The impact of using transfer learning on the problem of Urdu SA is also investigated in this article.