Sentiment Analysis on Government Performance in Tourism During The COVID-19 Pandemic Period With Lexicon Based
| Autor: | Adri Priadana, Ahmad Ashril Rizal |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Cauchy: Jurnal Matematika Murni dan Aplikasi, Vol 7, Iss 1, Pp 28-39 (2021) |
| Druh dokumentu: | article |
| ISSN: | 2086-0382 2477-3344 |
| DOI: | 10.18860/ca.v7i1.12488 |
| Popis: | The COVID-19 pandemic impact has affected all industries in Indonesia and even the world, including the tourism industry. Researchers have a role in researching to answer the needs of the tourism industry, especially in making tourism and business destination management programs and carrying out activities oriented to meet the needs of the tourism industry. Meanwhile, the government has a role in making policies, especially in the roadmap, for developing the tourism industry. This study aims to track trending topics in social media Instagram since COVID-19 hit. The results of trending topics will be classified by sentiment analysis using a Lexicon-based and Naive Bayes Classifier. Based on Instagram data taken since January 2020, it shows the five highest topics in the tourism sector, namely health protocols, hotels, homes, streets, and beaches. Of the five topics, sentiment analysis was carried out with the Lexicon-based and Naive Bayes classifier, showing that beaches get an incredibly positive sentiment, namely 80.87%, and hotels provide the highest negative sentiment 57.89%. The accuracy of the Confusion matrix's sentiment results shows that the accuracy, precision, and recall are 82.53%, 86.99%, and 83.43%, respectively. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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