Abstract
In our modern era, where the Internet is ubiquitous, everyone relies on various online resources for shopping and the increase in the use of social media platforms like Facebook, Twitter, etc. The user review spread rapidly among millions of users within a brief period. Consumer reviews on online products play a vital role in the selection of a product. The customer reviews are the measurement of customer satisfaction. This review data in terms of text can be analyzed to identify customers’ sentiment and demands. In this paper, we wish to perform four different classification techniques for various reviews available online with the help of artificial intelligence, natural language processing (NLP), and machine learning concepts. Moreover, a Web crawling methodology has also been proposed. Using this Web crawling algorithm, we can collect data from any website. We investigate and compare these techniques with the parameter of accuracy using different training data numbers and testing. Then we find the best classifier method based on accuracy.
Keywords
- Text classification
- Review
- NLP
- Machine learning
- Sentiment analysis
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Sagar, S.P., Oliullah, K., Sohan, K., Patwary, M.F.K. (2021). PRCMLA: Product Review Classification Using Machine Learning Algorithms. In: Kaiser, M.S., Bandyopadhyay, A., Mahmud, M., Ray, K. (eds) Proceedings of International Conference on Trends in Computational and Cognitive Engineering. Advances in Intelligent Systems and Computing, vol 1309. Springer, Singapore. https://doi.org/10.1007/978-981-33-4673-4_6
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DOI: https://doi.org/10.1007/978-981-33-4673-4_6
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