| Issue |
E3S Web Conf.
Volume 448, 2023
The 8th International Conference on Energy, Environment, Epidemiology and Information System (ICENIS 2023)
|
|
|---|---|---|
| Article Number | 02030 | |
| Number of page(s) | 10 | |
| Section | Information System | |
| DOI | https://doi.org/10.1051/e3sconf/202344802030 | |
| Published online | 17 November 2023 | |
Impact of Feature engineering for Improved Sentiment Analysis in Amazon Product Reviews Using K-Nearest Neighbor
1 Magister of Information System, School of Postgraduate Studies, Diponegoro University, Semarang 50275, Indonesia
2 Department of Statistics, Faculty of Science and Mathematics, Diponegoro University, Semarang 50275, Indonesia
3 Department of Mathematics, Faculty of Science and Mathematics, Diponegoro University, Semarang 50275, Indonesia
* Corresponding author: nitamiputri38@gmail.com
Online reviews are an important factor that encourages consumers to make purchases through e-commerce. However, it is challenging to objectively assess the sentiments expressed by actual consumers due to the prevalence of fraudulent reviews. This study focuses on sentiment analysis and seeks to uncover the best feature combinations based on review and reviewer centric approach. The results of the study show that the combination of feature Rating, VerifiedPurchase, ReviewLengths, and (CV+TF-IDF) = 1400 words with the application of KNN classification provides the best accuracy rate of 83%. The results of this study can assist consumers in making purchasing decisions and seller in increasing the value of their products and services based on the feedback provided by customers.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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