Applied and Computational Engineering
- The Open Access Proceedings Series for Conferences
Proceedings of the 4th International Conference on Computing and Data Science (CONF-CDS 2022)
Series Vol. 2 , 22 March 2023
Open
Access | Article
Sentimental Analysis on Product Reviews Using Support Vector Machine and Naïve Bayes
* Author to whom correspondence should be addressed.
Abstract
People nowadays use internet platforms to exchange ideas, share opinions, and learn online. Huge amounts of data are being poured into social media in the form of tweets, blogs, and updates on articles and items, among other things. The data is all unorganized and unprocessed. It is necessary to arrange and examine it. It takes a long time to analyze and process information using traditional methods and is impossible to analyze each and every sentence. So, there is a need to have a better approach. It can be done through sentimental analysis which extracts the opinion of a user in a piece of text data. This sentimental analysis will predict the polarity of the sentence, whether the given sentence is positive or a negative one. The sentimental analysis can be achieved through three approaches namely lexicon based, machine learning based and hybrid approach. This sentimental analysis is a part of NLP. This project aims to perform sentimental analysis using machine learning techniques and few natural language processing techniques on a product reviews dataset.
Keywords
Sentimental Analysis, SVM., Naive Bayes, Machine Learning, NLP
References
1. Singh, N. K., Tomar, D. S., &Sangaiah, A. K. (2020). Sentiment analysis: a review and com-parative analysis over social media. Journal of Ambient Intelligence and Humanized Com-puting, 11(1), 97-117.
2. Elmurngi, E. I., &Gherbi, A. (2018). Unfair reviews detection on amazon reviews using senti-ment analysis with supervised learning techniques. J. Comput. Sci., 14(5), 714-726.
3. Nawaz, U., Ali, A., Raza, U. A., &Shehzadi, K. (2021). A Survey: Sentimental Analysis on Product Reviews Using (MLT) Machine Learning Techniques and Approaches. Internation-al Journal, 10(2).
4. Zhang, Wei, Sui-xi Kong, and Yan-chun Zhu. "Sentiment classification and computing for online reviews by a hybrid SVM and LSA based approach." Cluster Computing, Volume 22, No. 5, 2019, pp. 12619-12632
5. Bhatt, A., Patel, A., Chheda, H., &Gawande, K. (2015). Amazon review classification and sentiment analysis. International Journal of Computer Science and Information Technolo-gies, 6(6), 5107-5110.
6. Chauhan, M., &Yadav, D. (2015). Sentimental analysis of product based reviews using ma-chine learning approaches. Journal of Network Communications and Emerging Technolo-gies (JNCET), 5(2), 19-25.
7. Bagheri, A., Saraee, M., & De Jong, F. (2013). Care more about customers: Unsupervised do-main-independent aspect detection for sentiment analysis of customer reviews. Knowledge-Based Systems, 52, 201-213.
8. Kumar, S., Gahalawat, M., Roy, P. P., Dogra, D. P., & Kim, B. G. (2020). Exploring impact of age and gender on sentiment analysis using machine learning. Electronics, 9(2), 374.
9. Appel, O., Chiclana, F., Carter, J., & Fujita, H. (2016). A hybrid approach to the sentiment analysis problem at the sentence level. Knowledge-Based Systems, 108, 110-124.
10. Singh, J., Singh, G., & Singh, R. “Optimization of sentiment analysis using machine learning classifiers”, Human-centric Computing and information Sciences, Volume 7, No 1, 32, 2017.
Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 4th International Conference on Computing and Data Science (CONF-CDS 2022)
- ISBN (Print)
- 978-1-915371-19-5
- ISBN (Online)
- 978-1-915371-20-1
- Published Date
- 22 March 2023
- Series
- Applied and Computational Engineering
- ISSN (Print)
- 2755-2721
- ISSN (Online)
- 2755-273X
- DOI
- 10.54254/2755-2721/2/20220586
- Copyright
- 22 March 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited