TY - GEN
T1 - Sentiment Analysis of Indonesian Temple Reviews Using Lexicon-Based Features and Stochastic Gradient Descent
AU - Noviani, Erina Fika
AU - Purwitasari, Diana
AU - Sholikah, Rizka Wakhidatus
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - TripAdvisor is an online platform where people can review tourist destinations. With so many reviews available, assessing public sentiment can be challenging. We conducted sentiment analysis on the review data from Indonesia's five most popular temples: Borobudur, Prambanan, Plaosan, Ijo, and Mendhut. Ratings on Tripadvisor reviews do not determine positive and negative sentences. Therefore, an automated method is necessary. Our research proposed using Lexicon-based and Stochastic Gradient Descent techniques for sentiment analysis. This study uses a lexicon-based because it can determine the weighting for the orientation of positive and negative sentiment words. SGD is used to build classification models because it can solve problems with large and complex datasets. The evaluation results showed that our method can significantly increase the model's performance. The result without a lexicon-based approach has an accuracy value of 70.3% while using a lexicon-based system improves the accuracy to 90.5%.
AB - TripAdvisor is an online platform where people can review tourist destinations. With so many reviews available, assessing public sentiment can be challenging. We conducted sentiment analysis on the review data from Indonesia's five most popular temples: Borobudur, Prambanan, Plaosan, Ijo, and Mendhut. Ratings on Tripadvisor reviews do not determine positive and negative sentences. Therefore, an automated method is necessary. Our research proposed using Lexicon-based and Stochastic Gradient Descent techniques for sentiment analysis. This study uses a lexicon-based because it can determine the weighting for the orientation of positive and negative sentiment words. SGD is used to build classification models because it can solve problems with large and complex datasets. The evaluation results showed that our method can significantly increase the model's performance. The result without a lexicon-based approach has an accuracy value of 70.3% while using a lexicon-based system improves the accuracy to 90.5%.
KW - Lexicon Based
KW - Sentiment Analysis
KW - Stochastic Gradient Descent
UR - http://www.scopus.com/inward/record.url?scp=85187233617&partnerID=8YFLogxK
U2 - 10.1109/ICITCOM60176.2023.10442938
DO - 10.1109/ICITCOM60176.2023.10442938
M3 - Conference contribution
AN - SCOPUS:85187233617
T3 - Proceeding - International Conference on Information Technology and Computing 2023, ICITCOM 2023
SP - 232
EP - 237
BT - Proceeding - International Conference on Information Technology and Computing 2023, ICITCOM 2023
A2 - Chen, Hsing-Chung
A2 - Damarjati, Cahya
A2 - Blum, Christian
A2 - Jusman, Yessi
A2 - Kanafiah, Siti Nurul Aqmariah Mohd
A2 - Ejaz, Waleed
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Conference on Information Technology and Computing, ICITCOM 2023
Y2 - 1 December 2023 through 2 December 2023
ER -
