Bi-LSTM Sentiment Classifier for Climate Change Issues in South Korea
Do-Yeon Kim1, Sung-Won Kang2, Seong-Taek Park3
1Do-Yeon Kim, Researcher, Korea Environment Institute, Sicheong-daero, Sejong-si, Republic of Korea.
2Sung-Won Kang, Senior Research Fellow, Korea Environment Institute, Sicheong-daero, Sejong-si, Republic of Korea.
3Seong-Taek Park, Professor, Sungkyunkwan University, Seonggyungwan-ro, Seoul, Republic of Korea.
Manuscript received on 18 August 2019 | Revised Manuscript received on 28 August 2019 | Manuscript Published on 16 September 2019 | PP: 295-299 | Volume-8 Issue-2S6 July 2019 | Retrieval Number: B10560782S619/2019©BEIESP | DOI: 10.35940/ijrte.B1056.0782S619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: A sentiment analysis using SNS data can confirm various people’s thoughts. Thus an analysis using SNS can predict social problems and more accurately identify the complex causes of the problem. In addition, big data technology can identify SNS information that is generated in real time, allowing a wide range of people’s opinions to be understood without losing time. It can supplement traditional opinion surveys. The incumbent government mainly uses SNS to promote its policies. However, measures are needed to actively reflect SNS in the process of carrying out the policy. Therefore this paper developed a sentiment classifier that can identify public feelings on SNS about climate change. To that end, based on a dictionary formulated on the theme of climate change, we collected climate change SNS data for learning and tagged seven sentiments. Using training data, the sentiment classifier models were developed using machine learning models. The analysis showed that the Bi-LSTM model had the best performance than shallow models. It showed the highest accuracy (85.10%) in the seven sentiments classified, outperforming traditional machine learning (Naive Bayes and SVM) by approximately 34.53%p, and 7.14%p respectively. These findings substantiate the applicability of the proposed Bi-LSTM-based sentiment classifier to the analysis of sentiments relevant to diverse climate change issues.
Keywords: Climate Change, Machine Learning, Bi-LSTM, CNN, Sentiment Classifier.
Scope of the Article: Machine Learning