Details
- Autor(en) / Beteiligte
- Titel
- Sentiment Analysis of Reviews in Natural Language: Roman Urdu as a Case Study
- Ist Teil von
- IEEE access, 2022, Vol.10, p.24945-24954
- Ort / Verlag
- Piscataway: IEEE
- Erscheinungsjahr
- 2022
- Link zum Volltext
- Quelle
- Electronic Journals Library
- Beschreibungen/Notizen
- Opinion Mining from user reviews is an emerging field. Sentiment Analysis of Natural Language text 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 text (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 text 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.
- Sprache
- Englisch
- Identifikatoren
- ISSN: 2169-3536eISSN: 2169-3536DOI: 10.1109/ACCESS.2022.3150172Titel-ID: cdi_proquest_journals_2637438814
- Format
- –
- Schlagworte
- Algorithms, ANN, Annotations, Artificial neural networks, Bayes methods, Benchmark testing, CNN, Data mining, decision tree, Deep learning, Filtration, K-NN, Machine learning, Natural language, Natural language processing, Naïve Bayes, Neural networks, Recurrent neural networks, RNN, Roman Urdu, Roman Urdu corpus, Sentiment analysis, sentiment classification, song reviews, supervised learning, Support vector machines, text classification, Videos