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Sentiment analysis for Urdu online reviews using deep learning models

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 learning model for the sentiments expressed in this under-resourced language. We develop an open-source corpus of 10,008 reviews from 566 online threads on the topics of sports, 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 utilizing another 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. To facilitate other researchers working in the same domain, we have open-sourced the corpus and code developed for this research

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