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Dosma Hutauruk
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Computer Science & IT

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Study of Public Sentiment Towards Beauty Products Using A Machine Learning Approach: Random Forest Analysis On Social Media Tresya Noviania Pasaribu; Tanjung, Juliansyah Putra; Dosma Hutauruk; Endang Sapriana Hutagalung; Saputra Silitonga
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13969

Abstract

In this digitalized era, the development of technology and the internet has brought significant changes in various aspects of life, including the way we shop. The trend of online shopping is increasingly prevalent and favored by the public, not least for cosmetic products. This research uses a quantitative approach to analyze public opinion or sentiment towards beauty products, especially beauty products. The data used in this research comes from online platforms. This research uses a beauty product dataset obtained from Kaggle. This research uses the Random Forest algorithm to analyze the data and produce findings, This algorithm is one of the advanced tools in Machine Learning that is focused on sorting data into the right categories, which in this context is used to classify public sentiment towards beauty products into categories such as positive, negative or neutral. Random Forest achieved a very high accuracy rate of 94.68% in the evaluation. However, it should be noted that the positive class has a low recall (25%) and a low F1-score (40%), indicating that the model may struggle to detect positive sentiment towards beauty products beauty products. In general, the model did well in classifying neutral and negative sentiments. Sentiment analysis shows that the majority of public sentiment towards beauty products is neutral, with a significant amount of negative and positive sentiment. It is evident that user opinions are informative or descriptive without conveying strong positive or negative emotions.
Co-Authors Endang Sapriana Hutagalung Juliansyah Putra Tanjung Saputra Silitonga Tresya Noviania Pasaribu