@article{scholars17755,
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
         journal = {IEEE Access},
           pages = {24945--24954},
            year = {2022},
           title = {Sentiment Analysis of Reviews in Natural Language: Roman Urdu as a Case Study},
          volume = {10},
            note = {cited By 13},
             doi = {10.1109/ACCESS.2022.3150172},
          author = {Qureshi, M. A. and Asif, M. and Hassan, M. F. and Abid, A. and Kamal, A. and Safdar, S. and Akber, R.},
            issn = {21693536},
        abstract = {Opinion Mining from user reviews is an emerging field. Sentiment Analysis of Natural Language helps us in finding the opinion of the customers. These reviews can be in any language e.g. English, Chinese, Arabic, Japanese, Urdu, and Hindi. This research presents a model to classify the polarity of the review(s) in Roman Urdu (reviews). For the purpose, raw data was scraped from the reviews of 20 songs from Indo-Pak Music Industry. In this research a new dataset of 24000 reviews of Roman Urdu is created. Nine Machine Learning algorithms - Na{\~A}?ve Bayes, Support Vector Machine, Logistic Regression, K-Nearest Neighbors, Artificial Neural Networks, Convolutional Neural Network, Recurrent Neural Networks, ID3 and Gradient Boost Tree, are attempted. Logistic Regression outperformed the rest, based on testing and cross validation accuracies that are 92.25 and 91.47 respectively. {\^A}{\copyright} 2013 IEEE.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124833996&doi=10.1109\%2fACCESS.2022.3150172&partnerID=40&md5=24218b44dcaaf385ddb4450b32d55a24},
        keywords = {Classification (of information); Data mining; Decision trees; Filtration; Learning algorithms; Music; Nearest neighbor search; Recurrent neural networks; Regression analysis; Support vector machines, ANN; Annotation; Benchmark testing; CNN; Deep learning; License; Machine-learning; Na\&\#x00ef;; RNN; Roman urdu; Roman urdu corpus; Sentiment analysis; Sentiment classification; Song review; Supervised learning; Text classification; Ve baye; Video, Sentiment analysis}
}