Sentiment Analysis of Online Customer Feedbacks Using NLP and Supervised Learning Algorithm

Authors

  • Kannagi Anbazhagan Associate Professor, Department of Computer Science and IT, Jain(Deemed-to-be University), Bangalore-27, India
  • Priyank Singhal Associate Professor, College of Computing Science and Information Technology, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, India
  • Mukur Gupta Assistant Professor, Department of Electrical Engineeing, Vivekananda Global University, Jaipur, India
  • Kumud Saxena Professor and HOD, Department of Computer Science & Engineering, Noida Institute of Engineering and Technology, Greater Noida, Uttar Pradesh, India

Keywords:

Machine learning, natural language processing, sentiment analysis, product reviews, sequential Bayesian linear regression (SBLR)

Abstract

E-Commerce is now an integral part of our everyday lives because of the proliferation of computers and the lightning-fast growth of the Internet. Numerous reviews are available for widely used products. In addition to making it harder for consumers to make an educated purchase choice, this also makes it harder for the product's producer to monitor and respond to customer feedback. One of the fastest-growing subfields in social media analysis is sentiment analysis and opinion mining. It's crucial since it gives companies a chance to concentrate on enhancing the company plan and learn more about the comments buyers have about their product. This research suggests using sequential Bayesian linear regression (SBLR) for Sentiment analysis of electronic customer comments. It includes mining a person's thoughts and feelings about a firm via computer analysis of customer purchasing patterns. The dataset used in this article has been gathered from Amazon and consists of customer opinions on various electrical products. We used machine learning (ML) techniques after analyzing the reviews to determine whether they were favorable or negative. The findings of this article indicate that ML Techniques provide the best outcomes for categorizing online Reviews.

Downloads

Download data is not yet available.

References

Wassan, S., Chen, X., Shen, T., Waqar, M. and Jhanjhi, N.Z., 2021. Amazon product sentiment analysis using machine learning techniques. Revista Argentina de Clínica Psicológica, 30(1), p.695.

Pahwa, B., Taruna, S. and Kasliwal, N., 2018. The sentiment is analysis-strategy for text pre-processing. Int. J. Comput. Appl, 180(34), pp.15-18.

Abualigah, L., Alfar, H.E., Shehab, M. and Hussein, A.M.A., 2020. Sentiment analysis in healthcare: a brief review. Recent advances in NLP: the case of Arabic language, pp.129-141.

Dhar, S., Pednekar, S., Borad, K. and Save, A., 2018, April. Sentiment analysis using neural networks: A new approach. In 2018 Second international conference on inventive communication and computational technologies (ICICCT) (pp. 1220-1224). IEEE.

Jagdale, R.S., Shirsat, V.S. and Deshmukh, S.N., 2019. Sentiment analysis on product reviews using machine learning techniques. In Cognitive Informatics and Soft Computing: Proceeding of CISC 2017 (pp. 639-647). Springer Singapore.

Tammina, S. and Annareddy, S., 2020, January. Sentiment analysis on customer reviews using convolutional neural network. In 2020 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1-6). IEEE.

Shafin, M.A., Hasan, M.M., Alam, M.R., Mithu, M.A., Nur, A.U. and Faruk, M.O., 2020, December. Product review sentiment analysis using NLP and machine learning in the Bangla language. In 2020 23rd International Conference on Computer and Information Technology (ICCIT) (pp. 1-5). IEEE.

Li, L., Johnson, J., Aarhus, W. and Shah, D., 2022. Key factors in MOOC pedagogy based on NLP sentiment analysis of learner reviews: What makes a hit. Computers & Education, 176, p.104354.

Adak, A., Pradhan, B. and Shukla, N., 2022. Sentiment analysis of customer reviews of food delivery services using deep learning and explainable artificial intelligence: Systematic review. Foods, 11(10), p.1500.

Mutinda, J., Mwangi, W. and Okeyo, G., 2023. Sentiment analysis of text reviews using lexicon-enhanced bert embedding (LeBERT) model with the convolutional neural network. Applied Sciences, 13(3), p.1445.

Sasikala, P. and Mary Immaculate Sheela, L., 2020. Sentiment analysis of online product reviews using DLMNN and future prediction of online product using IANFIS. Journal of Big Data, 7, pp.1-20.

Maitama, J.Z., Idris, N., Abdi, A., Shuib, L. and Fauzi, R., 2020. A systematic review on implicit and explicit aspect extraction in sentiment analysis. IEEE Access, 8, pp.194166-194191.

Kauffmann, E., Peral, J., Gil, D., Ferrández, A., Sellers, R. and Mora, H., 2019. Managing marketing decision-making with sentiment analysis: An evaluation of the main product features using text data mining. Sustainability, 11(15), p.4235.

Jagdale, R.S., Shirsat, V.S. and Deshmukh, S.N., 2019. Sentiment analysis on product reviews using machine learning techniques. In Cognitive Informatics and Soft Computing: Proceeding of CISC 2017 (pp. 639-647). Springer Singapore.

Kewsuwun, N. and Kajornkasirat, S., 2022. A sentiment analysis model of agritech startup on Facebook comments using naive Bayes classifier. International Journal of Electrical & Computer Engineering (2088-8708), 12(3).

Jain, P.K., Quamer, W., Saravanan, V. and Pamula, R., 2022. Employing BERT-DCNN with sentic knowledge base for social media sentiment analysis. Journal of Ambient Intelligence and Humanized Computing, pp.1-13.

Thakur, S. S. & Dela Cruz, G. B. (2022). Contribution of Social Robots to Interact with Humans and Others in Taking over Entire Job Functions, Such as Greeting and Basic Customer Service in the Workplace. Technoarete Transactions on Industrial Robotics and Automation Systems (TTIRAS), 2(2): 30–36.

C. S., S., Kunju, N. ., & Shahul Hameed, T. A. . (2023). Design and Simulation of Two Stage Wideband CMOS Amplifier in 90 NM Technology. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2s), 249–258. https://doi.org/10.17762/ijritcc.v11i2s.6144

Garcia, P., Martin, I., Garcia, J., Herrera, J., & Fernández, M. Enhancing Cyber security with Machine Learning-Based Intrusion Detection. Kuwait Journal of Machine Learning, 1(4). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/157

Downloads

Published

04.11.2023

How to Cite

Anbazhagan, K. ., Singhal, P. ., Gupta, M. ., & Saxena, K. . (2023). Sentiment Analysis of Online Customer Feedbacks Using NLP and Supervised Learning Algorithm. International Journal of Intelligent Systems and Applications in Engineering, 12(3s), 391–397. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3719

Issue

Vol. 12 No. 3s (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.