COMPARISON OF RANDOM FOREST AND NAÏVE BAYES CLASSIFIER METHODS IN SENTIMENT ANALYSIS ON CLIMATE CHANGE ISSUE
The last decade was recorded as a decade with a bad record on the issue of disasters in the world due to climate change. Measuring public opinion is one of the steps to mitigate the impact of climate change. Twitter is a popular social media for channeling opinions. Twitter provides a great source o...
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Universitas Pattimura
2023-09-01
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| Online Access: | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/8269 |
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| author | Fatkhurokhman Fauzi Wiwik Setiayani Tiani Wahyu Utami Eko Yuliyanto Iis Widya Harmoko |
| author_facet | Fatkhurokhman Fauzi Wiwik Setiayani Tiani Wahyu Utami Eko Yuliyanto Iis Widya Harmoko |
| author_sort | Fatkhurokhman Fauzi |
| collection | DOAJ |
| description | The last decade was recorded as a decade with a bad record on the issue of disasters in the world due to climate change. Measuring public opinion is one of the steps to mitigate the impact of climate change. Twitter is a popular social media for channeling opinions. Twitter provides a great source of data for understanding public opinion and the perceived risk of an issue. In recent decades, when discussing climate change, there are those who agree and those who oppose it. Sentiment analysis is a branch of learning in the realm of text mining that is used as a solution to see opinions on a problem, one of which is climate change. In this study, we will try to analyze opinions on climate change issues using the Random Forest and Naïve Bayes classifier methods. Data were obtained from Twitter for the period January 2022-June 2022. The training data used in this research is 80%:20%. There are slightly more positive statements than negative ones. The results obtained with the Naïve Bayes classifier method are an accuracy of 76.25%, an F-1 score of 78%, and a recall of 80%. While the results of the random forest method are 70.6% accuracy, 69% F-1 score, and 63% recall. The Nive Bayes method is better than the Random Forest method for classifying climate change opinions with an accuracy of 76.25%. |
| format | Article |
| id | doaj-art-e48f0ca1893b4cde8b8afba5b3748148 |
| institution | Kabale University |
| issn | 1978-7227 2615-3017 |
| language | English |
| publishDate | 2023-09-01 |
| publisher | Universitas Pattimura |
| record_format | Article |
| series | Barekeng |
| spelling | doaj-art-e48f0ca1893b4cde8b8afba5b37481482025-08-20T03:36:37ZengUniversitas PattimuraBarekeng1978-72272615-30172023-09-011731439144810.30598/barekengvol17iss3pp1439-14488269COMPARISON OF RANDOM FOREST AND NAÏVE BAYES CLASSIFIER METHODS IN SENTIMENT ANALYSIS ON CLIMATE CHANGE ISSUEFatkhurokhman Fauzi0Wiwik Setiayani1Tiani Wahyu Utami2Eko Yuliyanto3Iis Widya Harmoko4Department of Statistics, Faculty of Mathematics and Natural Science, Universitas Muhammadiyah Semarang, IndonesiaDepartment of Statistics, Faculty of Mathematics and Natural Science, Universitas Muhammadiyah Semarang, IndonesiaDepartment of Statistics, Faculty of Mathematics and Natural Science, Universitas Muhammadiyah Semarang, IndonesiaDepartment of Chemistry Education, Faculty of Mathematics and Natural Science, Universitas Muhammadiyah Semarang, IndonesiaMeteorology Climatology and Geophysics, IndonesiaThe last decade was recorded as a decade with a bad record on the issue of disasters in the world due to climate change. Measuring public opinion is one of the steps to mitigate the impact of climate change. Twitter is a popular social media for channeling opinions. Twitter provides a great source of data for understanding public opinion and the perceived risk of an issue. In recent decades, when discussing climate change, there are those who agree and those who oppose it. Sentiment analysis is a branch of learning in the realm of text mining that is used as a solution to see opinions on a problem, one of which is climate change. In this study, we will try to analyze opinions on climate change issues using the Random Forest and Naïve Bayes classifier methods. Data were obtained from Twitter for the period January 2022-June 2022. The training data used in this research is 80%:20%. There are slightly more positive statements than negative ones. The results obtained with the Naïve Bayes classifier method are an accuracy of 76.25%, an F-1 score of 78%, and a recall of 80%. While the results of the random forest method are 70.6% accuracy, 69% F-1 score, and 63% recall. The Nive Bayes method is better than the Random Forest method for classifying climate change opinions with an accuracy of 76.25%.https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/8269climate changenaïve bayes classifierrandom forestsentiment analysistext miningtwitter |
| spellingShingle | Fatkhurokhman Fauzi Wiwik Setiayani Tiani Wahyu Utami Eko Yuliyanto Iis Widya Harmoko COMPARISON OF RANDOM FOREST AND NAÏVE BAYES CLASSIFIER METHODS IN SENTIMENT ANALYSIS ON CLIMATE CHANGE ISSUE Barekeng climate change naïve bayes classifier random forest sentiment analysis text mining |
| title | COMPARISON OF RANDOM FOREST AND NAÏVE BAYES CLASSIFIER METHODS IN SENTIMENT ANALYSIS ON CLIMATE CHANGE ISSUE |
| title_full | COMPARISON OF RANDOM FOREST AND NAÏVE BAYES CLASSIFIER METHODS IN SENTIMENT ANALYSIS ON CLIMATE CHANGE ISSUE |
| title_fullStr | COMPARISON OF RANDOM FOREST AND NAÏVE BAYES CLASSIFIER METHODS IN SENTIMENT ANALYSIS ON CLIMATE CHANGE ISSUE |
| title_full_unstemmed | COMPARISON OF RANDOM FOREST AND NAÏVE BAYES CLASSIFIER METHODS IN SENTIMENT ANALYSIS ON CLIMATE CHANGE ISSUE |
| title_short | COMPARISON OF RANDOM FOREST AND NAÏVE BAYES CLASSIFIER METHODS IN SENTIMENT ANALYSIS ON CLIMATE CHANGE ISSUE |
| title_sort | comparison of random forest and naive bayes classifier methods in sentiment analysis on climate change issue |
| topic | climate change naïve bayes classifier random forest sentiment analysis text mining |
| url | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/8269 |
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