Abstract

Most existing studies are focused on popular languages like English, Spanish, Chinese,Japanese, and others, however, limited attention has been paid to Urdu despite having more than 60 million native speakers. In this paper, we develop a deep learningmodel for the sentiments expressed in this under-resourced language. We developan open-source corpus of 10,008 reviews from 566 online threads on the topics ofsports, food, software, politics, and entertainment. The objectives of this work are bifold (a) the creation of a human-annotated corpus for the research of sentiment analysis in Urdu; and (b) measurement of up-to-date model performance using a corpus.For their assessment, we performed binary and ternary classification studies utilizinganother model, namely long short-term memory (LSTM), recurrent convolutional neural network (RCNN) Rule-Based, N-gram, support vector machine , convolutional neural network, and LSTM. The RCNN model surpasses standard models with 84.98%accuracy for binary classification and 68.56% accuracy for ternary classification. Tofacilitate other researchers working in the same domain, we have open-sourced thecorpus and code developed for this research