Sentiment Analysis Untuk Review Destinasi Wisata Unggulan Gunung Kidul Menggunakan Metode Lexicon Dan Pivot

Abstract

Social media in providing freedom of access to information has a rapid effect on tourist destinations in Indonesia. A tweet may also contain information or conditions about tourist destinations that they will or have visited, such as visitor experiences in traveling, visitors' opinions of tourist attractions, and other tourist attractions. The condition of tourism globally, especially in the Gunungkidul area after the Covid-19 pandemic, has experienced a significant decline. This condition motivated researchers to conduct research with the aim of contributing to post-Covid-19 tourism development, in particular by conducting a sentiment analysis of comments by tourists visiting tourist areas in Gunungkidul Regency. Sentiment analysis is the process of using text analytics to derive various data sources from the internet and various social media platforms. Comment data obtained through social media Twitter. This classification is used to find out how comments about tourist attractions in Gunungkidul are. Lexicon based and pivoting are methods used to classify public opinion into three classes, namely positive, negative and neutral sentiments. This method is used to classify the results of community comment data written on the form of community satisfaction with tourism in Gunungkidul. This study aims to find out how people's opinions regarding tourism in Gunungkidul during the Covid-19 pandemic took place. The results of this study are used to determine the classification of community commentary data so that services can be easily developed through comments given by the public. The results of the sentiment analysis show that there are 51% positive emotion categories, 1% negative and 48% neutral.

Sentiment Analysis of Raja Ampat Tourism Destination Using CRISP-DM: SVM, NBC, DT, and k-NN Algorithm

“…[26] utilizes the Support Vector Machine (SVM) method to identify preferences for goods and services when traveling, using the sentiment approach of travelers visiting Ubud tourism destinations. On the other hand, [27] demonstrates how the branding of popular tourist spots is examined using the lexicon and pivot methodologies. This shows that sentiment analysis methods can be applied with classification algorithms like SVM, NBC, DT, and k-NN that are compatible with the characteristic of the dataset.…”

Post-Pandemic Society: Case Study of Gunungkidul Regency in Digital Tourism Transformation

“…Some of the efforts to digitize tourism in Gunungkidul after the pandemic were carried out with the help of the surrounding community as the main actor of digitization, in the form of utilizing digital marketing through social media and websites for the promotion of Telaga Jonge tourism (Andi Candra et al, 2022), implementing virtual tourism for the climbers of Mount Nglanggeran Ancient Volcano (Sekarsih & Mustopa, 2022), while media literacy, regional mapping, and digital branding of Langse Caves tourism has been considered as an effective promotional strategy (Pudrianisa, 2021). The integration of tourism promotion efforts with the technology and information sector after the pandemic has sparked research interest in collecting data on public sentiment about tourism destinations, where only 1% of comments on Gunungkidul were unfavorable, which is a remarkably good response for a region where tourism is a major industry (Larasati et al, 2022). In a comprehensive study of Gunungkidul potential tourists, Wijaya et al (2022) found that Gunungkidul's tourism spots are primarily visited by young female visitors, students, and employees aged 11 to 25.…”