Sentiment Analysis of Beauty Product E-Commerce Using
Support Vector Machine Method

Sentiment Analysis of Beauty Product E-Commerce Using
Support Vector Machine Method

classification, support vector machine, text mining, data mining

One of the important aspects of e-commerce is product quality, such as the quality of the trade and the application itself.
Customers who buy goods will provide an assessment in the form of a review. If an item is dominated by negative reviews,
other customers will be reluctant to buy at that store, so customers look for other stores and this affects the store's revenue.
Therefore, the purpose of this study is to classify e-commerce beauty product reviews using the Support Vector Machine to
create a model to categorize beauty product reviews and analyze accuracy. The research phase begins by collecting 50,000
datasets consisting of 35,000 training data and 15,000 test data. After the data is collected, the data labeling stage is carried
out which is labeled positive and negative. Then the preprocessing step is carried out so that the data is ready to be processed
in the feature extraction step. The feature extraction step aims to explore potential information that represents words.
Furthermore, the resulting data is evaluated to obtain an accuracy value and determine whether the model made is feasible to
use. The results showed that the Support Vector Machine can classify beauty product reviews well with an accuracy of 80.06%

Muhammad Rio Pratama1
, Faza Abdillah Gunawan S.2
, Rafdi Reyhan Zhafari3
, Rendy4
,
Helena Nurramdhani Irmanda

University of Pembangunan Nasional Veteran Jakarta

29-04-2022

Fajar bagus W

PDf

Indonesia

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