COMPARISON OF RANDOM FOREST AND NAÏVE BAYES CLASSIFIER METHODS IN SENTIMENT ANALYSIS ON CLIMATE CHANGE ISSUE

  • Fatkhurokhman Fauzi Department of Statistics, Faculty of Mathematics and Natural Science, Universitas Muhammadiyah Semarang, Indonesia
  • Wiwik Setiayani Department of Statistics, Faculty of Mathematics and Natural Science, Universitas Muhammadiyah Semarang, Indonesia
  • Tiani Wahyu Utami Department of Statistics, Faculty of Mathematics and Natural Science, Universitas Muhammadiyah Semarang, Indonesia
  • Eko Yuliyanto Department of Chemistry Education, Faculty of Mathematics and Natural Science, Universitas Muhammadiyah Semarang, Indonesia
  • Iis Widya Harmoko Meteorology Climatology and Geophysics, Indonesia
DOI: https://doi.org/10.30598/barekengvol17iss3pp1439-1448
Keywords: Climate Change, naïve bayes classifier, random forest, sentiment analysis, text mining, twitter

Abstract

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%.

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Published
2023-09-30
How to Cite
[1]
F. Fauzi, W. Setiayani, T. Utami, E. Yuliyanto, and I. Harmoko, “COMPARISON OF RANDOM FOREST AND NAÏVE BAYES CLASSIFIER METHODS IN SENTIMENT ANALYSIS ON CLIMATE CHANGE ISSUE”, BAREKENG: J. Math. & App., vol. 17, no. 3, pp. 1439-1448, Sep. 2023.
Issue
Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Section
Articles

Copyright (c) 2023 Fatkhurokhman Fauzi, Wiwik Setiayani, Tiani Wahyu Utami, Eko Yulianto, Iis Widya Harmoko

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