Implementation of Support Vector Machine Algorithm for Shopee Customer Sentiment Anlysis

Authors

  • Melda Betaria Sitepu Universitas Labuhanbatu, Indonesia
  • Ibnu Rasyid Munthe Universitas Labuhanbatu, Indonesia
  • Syaiful Zuhri Harahap Universitas Labuhanbatu, Indonesia

DOI:

10.33395/sinkron.v7i2.11408

Abstract

As the number one largest marketplace in Indonesia based on the criteria for the origin of international stores, Shopee must always improve the quality of its products and services based on reviews from users. Given the huge number of user reviews, it is not effective to identify them by reading one by one. For this reason, an automated system is needed that can read and identify reviews better. Sentiment analysis has proven to do the job. This study aims to conduct a sentiment analysis of shopee product reviews from users who use English. This study applies the Support Vector Machine algorithm to classify the Shopee user review data. To solve this problem, the research was carried out by going through several stages, namely: pre-processing the text of the dataset, performing feature extraction, after that the word weighting was carried out using the TF-IDF method, after clean data was obtained, the SVM algorithm was implemented, for further evaluation of the model. In the results of the study, it was found that the word that most represented the positive opinion of Shopee customers was "Good" with a total of 4684 words. While the word that represents the most negative opinion is "Seller" with 68 words. From the five sentiment analysis models tested, the average value of the confusion matrix is ​​obtained, which are precision=1, recall=0.97, and f1-score=0.98. From this research, it can be concluded that the SVM algorithm is proven to be applicable in conducting sentiment analysis on user reviews of Shopee products with an average accuracy rate of 97.3%.

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How to Cite

Sitepu, M. B. ., Munthe, I. R. ., & Harahap, S. Z. . (2022). Implementation of Support Vector Machine Algorithm for Shopee Customer Sentiment Anlysis. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 6(2), 619-627. https://doi.org/10.33395/sinkron.v7i2.11408

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