Analisis Sentimen Produk Skincare Somethinc Niacinamide di Female Daily dengan Naïve Bayes Classifier

Dwi Atmodjo WP, Januponsa Dio Firizqi, Zalfa Azzah Amalia

Abstract


One of the skincare products from Somethinc that is currently hotly discussed is Niacinamide Moisture Beet Serum. On the Female Daily site, people who have used the beauty products they use express their opinions in a review on Female Daily. However, the many thousands of reviews that exist have not been structured, so it is still difficult for the public and potential consumers to understand the essence of opinions on a product and classify them into the appropriate polarity of opinion. This research aims to analyze public sentiment regarding reviews of the Somethinc Niacinamide skincare product in Female Daily using the Naïve Bayes Classifier algorithm. This method was chosen because of its capabilities in probability-based text classification and popularity in sentiment analysis. The study began with collecting review data from the Female Daily site, followed by a text pre-processing process which included case folding, tokenizing, stopwords removal, and stemming. The dataset is then divided into training data (80%) and test data (20%). The Naïve Bayes Classifier algorithm is applied to classify reviews into positive, negative, or neutral categories. The research results show that this model is successful in classifying sentiment with 61% accuracy. The conclusions of this study indicate that although the Naïve Bayes Classifier algorithm is quite effective in classifying the sentiment of skincare product reviews, there is room for improvement, especially in handling meaningful and contextual words. This research provides new insight for brand owners and potential consumers in understanding public opinion regarding the Somethinc Niacinamide skincare product.

Keywords


Sentiment Analysis; Female Daily; Naïve Bayes Classifier; Review; Skincare

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References


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DOI: https://doi.org/10.30865/mib.v8i2.7571

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