Study Comparison Stemmer to Optimize Text Preprocessing In Sentiment Analysis Indonesian E-Commerce Reviews

Stemming was an essential part of text preprocessing. Stemming serves to map various forms of words into crucial words. Many stemming algorithms have been developed, including Indonesian stemmers. In this study, we will compare the rule-based stemmer algorithm and dictionary on sentiment analysis cl...

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Bibliographic Details
Published in2021 International Conference on Data Analytics for Business and Industry (ICDABI) pp. 135 - 139
Main Authors Faidha, Yunita Fatma, Shidik, Guruh Fajar, Fanani, Ahmad Zainul
Format Conference Proceeding
LanguageEnglish
Published IEEE 25.10.2021
Subjects
clasifications
Classification algorithms
Data analysis
Dictionaries
dictionary base
Electronic commerce
Industries
rule based
Sentiment analysis
stemming
Testing
text preprocesing
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Summary:Stemming was an essential part of text preprocessing. Stemming serves to map various forms of words into crucial words. Many stemming algorithms have been developed, including Indonesian stemmers. In this study, we will compare the rule-based stemmer algorithm and dictionary on sentiment analysis classification in the Indonesian E-Commerce Review. The purpose of this research is to find out the best preprocessing algorithm for K-Nearest Neighbor (KNN) classification. The results of the two stemmer testing still contain errors. KNN shows that the use of rule-based stemmers has an effect on increasing accuracy than without using a stemmer. In comparison, the dictionary-based method does not have much influence on the performance of the KNN method.
DOI:10.1109/ICDABI53623.2021.9655867