Analisis Bigdata Sentimen Ulasan Konsumen Masker Kesehatan Pada Marketplace
DOI:
https://doi.org/10.54066/jpsi.v2i1.1689Keywords:
Sentiment Analysis, Support Vector Machine, ClassificationAbstract
Big data is a collection of data that has a large volume, so traditional data processing technology is unable to handle it well. Marketplace is a platform that most people often use to shop online. On this platform there is a comments column for writing reviews of products that have been purchased. Consumer reviews have an important role in understanding customer perceptions and sentiments towards the products being sold. Classification uses the Support Vector Machine method. The goal is to classify consumer reviews into positive or negative sentiment categories. The test uses data from more than 300 data samples with the assumption of independence between the features in the data. The results of big data analysis of consumer sentiment reviews of health masks on the marketplace used the support vector machine method with an accuracy value of 88%. The results of the analysis can be concluded that the dominant results of scraping reviews on health mask products lead more to positive reviews. The results on wordcloud of negative reviews provide insight to improve the quality of masks which are still lacking in terms of thinness, straps breaking easily, tears, holes, rubber quality and product packaging.
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