Classification of Product Review Sentiment by NLP and Machine Learning
2nd International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2022
; 2022.
Article
in English
| Scopus | ID: covidwho-1961385
ABSTRACT
Online marketing and e-commerce firms were already prospering in Bangladesh during this era of internet technology. Because people are under lockdown due to the COVID-19 epidemic, internet shopping has become the major platform for purchasing because it is the safest option. It sped up the time it took for firms to go online. More online product service providers improve people's lives, but it also raises concerns about product quality and service. As a result, it is simple for new clients to dupe while purchasing online. Our objective is to create a system that uses Natural Language Processing to assess client feedback from online purchasing and deliver a ratio of good and bad comments written in Bangla from past customers (NLP). We gathered approximately 6000 comments and views on the product to conduct the study. As classification approaches, we used sentiment analysis, as well as KNN, Decision Tree, Support Vector Machine (SVM), Random Forest, and Logistic Regression. With an accuracy of 94.78 percent, SVM outperformed all other methods. © 2022 IEEE.
Decision Tree; kNN; Logistic Regression; Machine learning; Naïve Bayes; NLP; Product Review; Random Forest; Support Vector Machine; Decision trees; Electronic commerce; Sales; Sentiment analysis; Support vector regression; E- commerces; Logistics regressions; Machine-learning; Naive bayes; Online marketing; Product reviews; Product service; Random forests; Support vectors machine
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
2nd International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2022
Year:
2022
Document Type:
Article
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