Cross Domain Sentiment Classification for Urdu Language

Abstract

An enormous amount of information is produced daily on the internet about different prod ucts and objects. People like to express their feelings, thoughts in their native language on different social sites. This bulk data needs to be interpreted. So, Sentiment Analysis (SA) is required which extracts people’s opinions, feelings, and thoughts. However, it is a highly domain-dependent task. Due to this, the issue of domain-transfer arises. If a classifier is tested with any different domain, other than on which it is trained, its performance is affected. Many tasks and frameworks are created in mostly English and western languages. Tasks intended for the English language cannot be applied for other languages, hence there is a need to work on different dialects. In this research, we performed a cross-domain sentiment Analysis on data set of Urdu language comprising of 9000 sentences from sports tweets in which there are two domains (Hockey and Cricket). Furthermore, preparing corpus for specific domains in the Urdu language we applied ma chine learning and deep learning approach. After this, we evaluated results using standard evaluation measures and a confusion matrix, Gated Recurrent Units (GRU) gives the highest accuracy of (77%)