Scholarly PublicationQureshi, M.A. and Asif, M. and Hassan, M.F. and Abid, A. and Kamal, A. and Safdar, S. and Akber, R. (2022) Sentiment Analysis of Reviews in Natural Language: Roman Urdu as a Case Study. IEEE Access, 10. pp. 24945-24954.
Full text not available from this repository.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 - Naïve 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. © 2013 IEEE.
| Item Type: | Article |
|---|---|
| Impact Factor: | cited By 1 |
| Uncontrolled 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; Naï; RNN; Roman urdu; Roman urdu corpus; Sentiment analysis; Sentiment classification; Song review; Supervised learning; Text classification; Ve baye; Video, Sentiment analysis |
| Depositing User: | Mr Ahmad Suhairi Mohamed Lazim |
| Date Deposited: | 07 Sep 2022 08:32 |
| Last Modified: | 07 Sep 2022 08:32 |
| URI: | http://scholars.utp.edu.my/id/eprint/33645 |
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