Fadly, . and Dewi Marlina, . and Tri Basuki, Kurniawan and Mohd Zaki, Zakaria and Siti Farahnasihah, Abdullah (2022) Sentiment Analysis on Natural Skincare Products. Journal of Data Science, 2022 (12). pp. 1-17. ISSN 2805-5160
Text
jods2022_12.pdf - Published Version Available under License Creative Commons Attribution. Download (727kB) |
Abstract
Skincare Industry was increasing rapidly year by year. In contrast, many skincare companies have brought their products and originality to attract many customers. However, due to many controversial cases involving the chemical substance in skincare products, the company switched to something more natural: natural skincare. With much natural skincare in the shop, many customers face the problem of which one to buy. This research helps customers by giving a guideline for the customers to make the decision. Sentiment Analysis is used to analyze the reviews from past customers and create a visualization containing positivity and negativity of all the reviews. Five classifiers were used to produce the best result: Naïve Bayes, KNN, SVM, Decision Tree, and Deep Learning. The reviews were collected from Sephora.com websites, and the tools used in analyzing the reviews are Python and RapidMiner. Reviews collected are 10000 data from a website. The result shows that Deep Learning and Decision Tree are classifiers in sentiment analysis with almost 80% accuracy and 60% F1 measurement. F1 measure is a measure of a test's accuracy. For future enhancements, the data collected can be more than this research, and no data imbalance was created.
Item Type: | Article |
---|---|
Uncontrolled Keywords: | Sentiment Analysis, Natural Skincare, Python, RapidMiner |
Subjects: | H Social Sciences > HF Commerce Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software |
Depositing User: | Unnamed user with email masilah.mansor@newinti.edu.my |
Date Deposited: | 01 Sep 2022 15:01 |
Last Modified: | 07 May 2024 09:47 |
URI: | http://eprints.intimal.edu.my/id/eprint/1667 |
Actions (login required)
View Item |