A Technique for Tourism Visitor Feedback Collection Based on Google Maps Reviews Using Text-Mining

DOI

10.2991/978-94-6463-445-7_9How to use a DOI?

Keywords

Tourism feedback analysis; text mining in tourism; sentiment and topic detection

Abstract

Jember Regency has enormous tourism potential which has the potential to be developed into a potential superior area. However, developing this potential is not easy considering that feedback on satisfaction with the services provided at tourist locations is also not easy to obtain. This research aims to develop a technique for collecting feedback, especially for services that are felt to be lacking, using a text-mining approach based on visitor reviews on Google Maps. This technique is based on Support Vector Machine as a sentiment classification algorithm, Latent Dirichlet Allocation (LDA) to detect topics, and Perceptron Tagger as a POS Tagger algorithm to recognize services that are considered lacking. The evaluation of this technique is promising because the classification and POS Tagger model in this research have a high level of accuracy, i.e., 92% and 96.6%, so they are good enough to be implemented. It is shown that based on review data from Google Maps from 2020 to 2023 in eight tourism areas in Jember Regency the entrance ticket price for Papuma Beach and Jember Mini Zoo is considered expensive, while Watu Ulo Beach, Payangan Beach, Pancer Beach, and Sukorambi Botanical Gardens are considered the tourist attractions are dirty, and Pancer Beach and the Gunung Gambir Tea Garden are difficult to access.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Achmad Maududie
AU  - Aula Fajrun Khalilurahman
AU  - Priza Pandunata
PY  - 2024
DA  - 2024/06/29
TI  - A Technique for Tourism Visitor Feedback Collection Based on Google Maps Reviews Using Text-Mining
BT  - Proceedings of the 2nd International Conference on Neural Networks and Machine Learning 2023 (ICNNML 2023)
PB  - Atlantis Press
SP  - 72
EP  - 83
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-445-7_9
DO  - 10.2991/978-94-6463-445-7_9
ID  - Maududie2024
ER  -