BERS: Bussiness-Related Emotion Recognition System in Urdu Language Using Machine Learning
Lqra Sana, Khushboo Nasir, Amara Urooj, Zain Ishaq, Ibrahim A. Hameed · 2018
Starting a business is an easy task but making it established and reliable is something challenging. Any business can grow if the customers are satisfied and this can be investigated through their emotions or reviews expressed about the goods and services. It gives rise to the development of emotion recognition system from business reviews using social computing paradigm. A sufficient work has already been performed in this direction using resource-rich languages like English. However, there is a need and a literature gap to develop such a system in Urdu, a resource-poor language, which is a national language of Pakistan and a widely spoken language in other countries like India and other parts of the world. This work aims at developing an Emotion detection System from online business reviews (tweets) in Urdu Language using supervised Machine Learning techniques. We applied different machine learning classifiers, such as Support Vector Classifier (SVC), Random Forest (RF), Naïve Bayes (NB) and K-Nearest Neighbors (KNN) to classify the tweets with respect to Urdu emotions. Results show that with respect to other classifiers, SVC achieved efficient results with an accuracy of 80.5% on smart phone dataset and 81.09% for sports dataset.