Abstract

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.

Original languageEnglish
Title of host publicationProceedings of 2023 IEEE International Conference on Data and Software Engineering, ICoDSE 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages19-24
Number of pages6
ISBN (Electronic)9798350381382
DOIs
Publication statusPublished - 2023
Event2023 IEEE International Conference on Data and Software Engineering, ICoDSE 2023 - Hybrid, Toba, Indonesia
Duration: 7 Sept 20238 Sept 2023

Publication series

NameProceedings of 2023 IEEE International Conference on Data and Software Engineering, ICoDSE 2023

Conference

Conference2023 IEEE International Conference on Data and Software Engineering, ICoDSE 2023
Country/TerritoryIndonesia
CityHybrid, Toba
Period7/09/238/09/23

Keywords

  • local sentiment aggregation
  • sentiment analysis
  • tourism

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