Open Access iconOpen Access



Sentiment Analysis of Roman Urdu on E-Commerce Reviews Using Machine Learning

Bilal Chandio1, Asadullah Shaikh2, Maheen Bakhtyar1, Mesfer Alrizq2, Junaid Baber1, Adel Sulaiman2,*, Adel Rajab2, Waheed Noor3

1 Department of Computer Science and Information Technology, University of Balochistan, Quetta, 87300, Pakistan
2 College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia
3 Department of Information Technology, University of Balochistan, Quetta, 87300, Pakistan

* Corresponding Author: Adel Sulaiman. Email: email

Computer Modeling in Engineering & Sciences 2022, 131(3), 1263-1287.


Sentiment analysis task has widely been studied for various languages such as English and French. However, Roman Urdu sentiment analysis yet requires more attention from peer-researchers due to the lack of Off-the-Shelf Natural Language Processing (NLP) solutions. The primary objective of this study is to investigate the diverse machine learning methods for the sentiment analysis of Roman Urdu data which is very informal in nature and needs to be lexically normalized. To mitigate this challenge, we propose a fine-tuned Support Vector Machine (SVM) powered by Roman Urdu Stemmer. In our proposed scheme, the corpus data is initially cleaned to remove the anomalies from the text. After initial pre-processing, each user review is being stemmed. The input text is transformed into a feature vector using the bag-of-word model. Subsequently, the SVM is used to classify and detect user sentiment. Our proposed scheme is based on a dictionary based Roman Urdu stemmer. The creation of the Roman Urdu stemmer is aimed at standardizing the text so as to minimize the level of complexity. The efficacy of our proposed model is also empirically evaluated with diverse experimental configurations, so as to fine-tune the hyper-parameters and achieve superior performance. Moreover, a series of experiments are conducted on diverse machine learning and deep learning models to compare the performance with our proposed model. We also introduced the largest dataset on Roman Urdu, i.e., Roman Urdu e-commerce dataset (RUECD), which contains 26K+ user reviews annotated by the group of experts. The RUECD is challenging and the largest dataset available of Roman Urdu. The experiments show that the newly generated dataset is quite challenging and requires more attention from the peer researchers for Roman Urdu sentiment analysis.


Cite This Article

Chandio, B., Shaikh, A., Bakhtyar, M., Alrizq, M., Baber, J. et al. (2022). Sentiment Analysis of Roman Urdu on E-Commerce Reviews Using Machine Learning. CMES-Computer Modeling in Engineering & Sciences, 131(3), 1263–1287.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 3319


  • 1270


  • 0


Share Link