Sentiment Analysis on Government Performance in Tourism During The COVID-19 Pandemic Period With Lexicon Based
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 act...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Mathematics Department UIN Maulana Malik Ibrahim Malang
2021-11-01
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| Series: | Cauchy: Jurnal Matematika Murni dan Aplikasi |
| Subjects: | |
| Online Access: | https://ejournal.uin-malang.ac.id/index.php/Math/article/view/12488 |
| _version_ | 1818200586262151168 |
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| author | Adri Priadana Ahmad Ashril Rizal |
| author_facet | Adri Priadana Ahmad Ashril Rizal |
| author_sort | Adri Priadana |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-12T02:40:01Z |
| format | Article |
| id | doaj.art-7b4342f051f94b2b8b90ba0161ffb5a7 |
| institution | Directory Open Access Journal |
| issn | 2086-0382 2477-3344 |
| language | English |
| last_indexed | 2024-12-12T02:40:01Z |
| publishDate | 2021-11-01 |
| publisher | Mathematics Department UIN Maulana Malik Ibrahim Malang |
| record_format | Article |
| series | Cauchy: Jurnal Matematika Murni dan Aplikasi |
| spelling | doaj.art-7b4342f051f94b2b8b90ba0161ffb5a72022-12-22T00:41:11ZengMathematics Department UIN Maulana Malik Ibrahim MalangCauchy: Jurnal Matematika Murni dan Aplikasi2086-03822477-33442021-11-0171283910.18860/ca.v7i1.124885875Sentiment Analysis on Government Performance in Tourism During The COVID-19 Pandemic Period With Lexicon BasedAdri Priadana0Ahmad Ashril Rizal1Universitas Jenderal Achmad Yani YogyakartaUniversitas Islam Negeri MataramThe 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.https://ejournal.uin-malang.ac.id/index.php/Math/article/view/12488sentiment analysisgovernment performance in tourismcovid-19 pandemic periodlexicon based |
| spellingShingle | Adri Priadana Ahmad Ashril Rizal Sentiment Analysis on Government Performance in Tourism During The COVID-19 Pandemic Period With Lexicon Based Cauchy: Jurnal Matematika Murni dan Aplikasi sentiment analysis government performance in tourism covid-19 pandemic period lexicon based |
| title | Sentiment Analysis on Government Performance in Tourism During The COVID-19 Pandemic Period With Lexicon Based |
| title_full | Sentiment Analysis on Government Performance in Tourism During The COVID-19 Pandemic Period With Lexicon Based |
| title_fullStr | Sentiment Analysis on Government Performance in Tourism During The COVID-19 Pandemic Period With Lexicon Based |
| title_full_unstemmed | Sentiment Analysis on Government Performance in Tourism During The COVID-19 Pandemic Period With Lexicon Based |
| title_short | Sentiment Analysis on Government Performance in Tourism During The COVID-19 Pandemic Period With Lexicon Based |
| title_sort | sentiment analysis on government performance in tourism during the covid 19 pandemic period with lexicon based |
| topic | sentiment analysis government performance in tourism covid-19 pandemic period lexicon based |
| url | https://ejournal.uin-malang.ac.id/index.php/Math/article/view/12488 |
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