Implementation of n-gram Methodology to Analyze Sentiment Reviews for Indonesian Chips Purchases in Shopee E-Marketplace
Dublin Core
Title
Implementation of n-gram Methodology to Analyze Sentiment Reviews for Indonesian Chips Purchases in Shopee E-Marketplace
Subject
N-gram; sentiment analysis; shopee; support vector machine
Description
Chips are a well-known product among Small and Medium Enterprises (SMEs). In order to enhance the quality of chips as
an SME product, sentiment analysis is a crucial step. In this research, sentiment analysis of chip purchases on the Shopee Emarketplace was conducted using the Natural Language Processing (NLP) method, utilizing the N-Gram Model and Term
Frequent-Inverse Document Frequency (TF-IDF) as feature extraction techniques, and the Support Vector Machine (SVM)
algorithm for sentiment classification. The objective of this research is to identify the most suitable feature extraction model
and optimal SVM kernel type from the options of Linear, Polynomial degree, Gaussian RBF, and Sigmoid kernels. Results
from the experiments indicate that the TF-IDF and unigram feature extraction techniques offer the best performance for SVM
classification when utilizing the Linear kernel. By labeling the dataset, it was observed that using a lexicon-based approach
for sentiment classification resulted in 84.31% of the total reviews being positive. The words "price", "cheap" and "quality"
in unigram have the highest weights above 0.040. In the unigram model, linear kernel accuracy and precision performance
values are 88.4% and 87.3%. At the same time, the recall performance values is 88.4%. The results of the F1-Score
assessment matrix from Unigram were 86.9%, Bigram was 78.5% and Trigram was 77.4%. Ultimately, the unigram model
combined with a linear kernel in the SVM algorithm demonstrates strong potential for application in the development of
various systems focused on detecting user reviews in the Indonesian language on the Shopee E-Marketplace
an SME product, sentiment analysis is a crucial step. In this research, sentiment analysis of chip purchases on the Shopee Emarketplace was conducted using the Natural Language Processing (NLP) method, utilizing the N-Gram Model and Term
Frequent-Inverse Document Frequency (TF-IDF) as feature extraction techniques, and the Support Vector Machine (SVM)
algorithm for sentiment classification. The objective of this research is to identify the most suitable feature extraction model
and optimal SVM kernel type from the options of Linear, Polynomial degree, Gaussian RBF, and Sigmoid kernels. Results
from the experiments indicate that the TF-IDF and unigram feature extraction techniques offer the best performance for SVM
classification when utilizing the Linear kernel. By labeling the dataset, it was observed that using a lexicon-based approach
for sentiment classification resulted in 84.31% of the total reviews being positive. The words "price", "cheap" and "quality"
in unigram have the highest weights above 0.040. In the unigram model, linear kernel accuracy and precision performance
values are 88.4% and 87.3%. At the same time, the recall performance values is 88.4%. The results of the F1-Score
assessment matrix from Unigram were 86.9%, Bigram was 78.5% and Trigram was 77.4%. Ultimately, the unigram model
combined with a linear kernel in the SVM algorithm demonstrates strong potential for application in the development of
various systems focused on detecting user reviews in the Indonesian language on the Shopee E-Marketplace
Creator
M. Eka Purbaya, Diovianto Putra Rakhmadani, Maliana Puspa Arum, Luthfi Zian Nasifah
Source
: http://jurnal.iaii.or.id
Publisher
Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association
Date
June 2023
Contributor
Sri Wahyuni
Rights
ISSN Media Electronic: 2580-0760
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
M. Eka Purbaya, Diovianto Putra Rakhmadani, Maliana Puspa Arum, Luthfi Zian Nasifah, “Implementation of n-gram Methodology to Analyze Sentiment Reviews for Indonesian Chips Purchases in Shopee E-Marketplace,” Repository Horizon University Indonesia, accessed January 11, 2026, https://repository.horizon.ac.id/items/show/10007.