Emotions Analysis of Tourist Using Lexicon Text Analysis
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Abstract
Travelers often write comments on the internet, usually about experiences, opinions, and even complaints. Comment data on the internet can provide information for stakeholders. This information can be extracted using text analysis methods such as positive and negative sentiments. Sentiments can be detailed into eight types of emotions. This study aims to extract emotions from tourists' comments on Google Map, especially on tourist-site accounts in BARLINGMASCAKEB. The dataset comments were crawled from ten tourism objects in BARLINGMASCAKEB. The method used is Lexicon Emotion Analysis. The results show that the majority of tourists have positive experiences. It is shown by the emotion "joy" and "trust." Emotions "joy" and "trust" have positive meanings, so it can be said that the majority of tourists feel positive emotions. There are sites that present highest emotions of "joy": Aquarium-Purbasari-Pancuran-Mas with 33.52%, Lembah-Asri-Serang with 30.85%, Sanggaluri-Reptile-Park by 30, 27%, Baturaden Botanical-Gardens with 27, 67 %, and Curug-Jenggala by 23.4%. At the same time, the highest types of "trust" emotions are Benteng-Pandem with 27.41%, Arjuna-Temple with 26.6%, Sikidang-Crater with 20.71%, and Menganti-Beach with 25, 74%. Only one site, the World Miniature Park, gives the highest "anticipation" emotion. Usually, caring words represent anticipation emotions, so they can still be categorized into positive emotions. The extraction of emotions is affected by the process of emotion-labeling of each comment, so further research is recommended to develop a lexicon emotion dictionary. The results of this study are expected to provide benefits for the development of the tourism industry in the BARLINGMASCAKEB area and for the academic world, especially regarding the application of text mining in the tourism sector
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[1]
D. C. . Rahmadani, S. Khomsah, and M. Y. Fathoni, “Emotions Analysis of Tourist Using Lexicon Text Analysis ”, JuTISI, vol. 10, no. 1, pp. 13 –, May 2024.
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial used, distribution and reproduction in any medium.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.