TY - GEN
T1 - Aspect-Based Sentiment Analysis Model with Local Sentiment Aggregation for Online Travel Reviews
AU - Kusumawardani, Renny Pradina
AU - Arrizal Kusuma, Moch Farrel
AU - Wibowo, Radityo Prasetianto
AU - Tjahyanto, Aris
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Economic growth is a crucial indicator for any country's economy. Tourism plays a vital role in the national economy of Indonesia with its multiplier effect, influencing the development of other sectors. Aspect-based sentiment analysis has emerged as a popular approach for understanding the opinions of tourist reviews. However, most aspect-identification algorithms are based on sentence dependencies, which are computationally expensive for longer texts. Therefore, in this paper, we studied Local Sentiment Aggregation (LSA) as an efficient method to extract aspects and classify sentiment polarities of these aspects. LSA introduces aggregation window-based sentiment learning (AW), a mechanism based on embeddings for neighbouring words. The results of our research demonstrate that the LSA model performs well on tourism reviews, as shown by the model's accuracy, which reached 92.48% and an F1 score of 87.40%. copy; 2023 IEEE.
AB - Economic growth is a crucial indicator for any country's economy. Tourism plays a vital role in the national economy of Indonesia with its multiplier effect, influencing the development of other sectors. Aspect-based sentiment analysis has emerged as a popular approach for understanding the opinions of tourist reviews. However, most aspect-identification algorithms are based on sentence dependencies, which are computationally expensive for longer texts. Therefore, in this paper, we studied Local Sentiment Aggregation (LSA) as an efficient method to extract aspects and classify sentiment polarities of these aspects. LSA introduces aggregation window-based sentiment learning (AW), a mechanism based on embeddings for neighbouring words. The results of our research demonstrate that the LSA model performs well on tourism reviews, as shown by the model's accuracy, which reached 92.48% and an F1 score of 87.40%. copy; 2023 IEEE.
KW - local sentiment aggregation
KW - sentiment analysis
KW - tourism
UR - http://www.scopus.com/inward/record.url?scp=85177462916&partnerID=8YFLogxK
U2 - 10.1109/ICoDSE59534.2023.10291572
DO - 10.1109/ICoDSE59534.2023.10291572
M3 - Conference contribution
AN - SCOPUS:85177462916
T3 - Proceedings of 2023 IEEE International Conference on Data and Software Engineering, ICoDSE 2023
SP - 19
EP - 24
BT - Proceedings of 2023 IEEE International Conference on Data and Software Engineering, ICoDSE 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Data and Software Engineering, ICoDSE 2023
Y2 - 7 September 2023 through 8 September 2023
ER -
