TY - GEN
T1 - Sentiment analysis of IPOT application reviews using naïve bayes method
AU - Kurniasari, Nur Rochmah
AU - Wibowo, Wahyu
N1 - Publisher Copyright:
© 2022 American Institute of Physics Inc.. All rights reserved.
PY - 2022/10/11
Y1 - 2022/10/11
N2 - Investment conditions in Indonesia today are very crowded among the public, especially investors as investment actors. One of the investment applications used by the people of Indonesia is IPOT. IPOT is a trading application for stock investments, mutual funds, and Exchanged Traded Funds (ETF) with more complete features. The availability of IPOT application user reviews on Google Play is very helpful for companies to know the needs and wants of users about the application. These reviews can be analyzed using sentiment analysis to find out how users think about the application they're using. Therefore, the purpose of this study is to conduct sentiment analysis on IPOT application reviews using the Naive Bayes Classifier method. The results of this study were obtained that positive sentiment has a greater value compared to negative sentiment with the accuracy of the classification of test data has an AUC value of 67.80% while the training data has an AUC value of 70.13%.
AB - Investment conditions in Indonesia today are very crowded among the public, especially investors as investment actors. One of the investment applications used by the people of Indonesia is IPOT. IPOT is a trading application for stock investments, mutual funds, and Exchanged Traded Funds (ETF) with more complete features. The availability of IPOT application user reviews on Google Play is very helpful for companies to know the needs and wants of users about the application. These reviews can be analyzed using sentiment analysis to find out how users think about the application they're using. Therefore, the purpose of this study is to conduct sentiment analysis on IPOT application reviews using the Naive Bayes Classifier method. The results of this study were obtained that positive sentiment has a greater value compared to negative sentiment with the accuracy of the classification of test data has an AUC value of 67.80% while the training data has an AUC value of 70.13%.
UR - http://www.scopus.com/inward/record.url?scp=85140239870&partnerID=8YFLogxK
U2 - 10.1063/5.0116704
DO - 10.1063/5.0116704
M3 - Conference contribution
AN - SCOPUS:85140239870
T3 - AIP Conference Proceedings
BT - 3rd International Conference on Mathematics and Sciences, ICMSc 2021
A2 - Nugroho, Rudy Agung
A2 - Allo, Veliyana Londong
A2 - Siringoringo, Meiliyani
A2 - Prangga, Surya
A2 - Wahidah, null
A2 - Munir, Rahmiati
A2 - Hiyahara, Irfan Ashari
PB - American Institute of Physics Inc.
T2 - 3rd International Conference on Mathematics and Sciences 2021: A Brighter Future with Tropical Innovation in the Application of Industry 4.0, ICMSc 2021
Y2 - 12 October 2021 through 13 October 2021
ER -