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Author: search.filters.author.Boonseng, ChongragSubject: search.filters.subject.Support vector machine

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    Sentiment analysis of the awareness of environmental sustainability
    (2024-01-01)
    Kularbphettong, Kunyanuth
    ;
    Roonrakwit, Pattarapan
    ;
    Boonseng, Chongrag
    This study examines the sentiment analysis of awareness of environmental sustainability. Environmental sustainability is the responsible management and utilization of Earth's natural resources to meet the needs of the present generation and ensure that future generations will access those resources. The awareness of environmental sustainability has been growing globally as people, businesses, and governments recognize the importance of preserving the planet for current and future generations. Sentiment analysis of environmental sustainability involves evaluating opinions, attitudes, and emotions expressed in texts related to environmental sustainability, and analyzing sentiment can provide insights into public perception, awareness, and engagement with environmental issues. This exploratory study's primary goal is to conduct social media opinion mining in the context of Thai people's environmental sustainability. The paper presented how to build a model of sentiment analysis with linguistic analysis, including data preprocessing steps, feature extraction, and model constructions. The techniques used in this research include Logistic Regression, Random Forests, Support Vector Machine, Word Segmentation and Bag of Words. The result shows that the model is able to categorize sentiment analysis opinions in the sustainability context primarily in positive terms. The positive sentiments suggest a sustained, long-term shift in awareness, or they might be influenced by specific events or trends. However, positive sentiment analysis results are expressed towards environmental sustainability initiatives, such as renewable energy projects, waste reduction efforts, or conservation programs. Moreover, public awareness plays a crucial role in influencing individual behavior, corporate practices, and government policies towards a more sustainable and environmentally conscious future.
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    Prediction of CO2 emissions using machine learning
    (2024-01-01)
    Bussaban, Kanyarat
    ;
    Kularbphettong, Kunyanuth
    ;
    Raksuntorn, Nareenart
    ;
    Boonseng, Chongrag
    Carbon dioxide (CO<inf>2</inf>) contributes significantly to climate change as a greenhouse gas. The Earth's atmosphere is naturally kept warm enough to support life by greenhouse gases which trap heat in the atmosphere. However, human activity has significantly increased the amount of CO2 in the atmosphere because of deforestation and the use of fossil fuels. One of the key concerns with human evolution that fuels global climate change is carbon dioxide (CO<inf>2</inf>). It is released as fuels burn and as a result, people worldwide are gradually becoming more conscious of environmental issues. Effective policy formulation requires an investigation of the factors influencing CO<inf>2</inf> emissions, yet tiny datasets and traditional research methodologies have hampered prior investigations. This research uses three prediction models to estimate CO<inf>2</inf> trapping efficiency among CO<inf>2</inf> emissions, energy use and GDP: Multiple Linear Regression (MLR), Support Vector Machine (SVM) and Random Forest (RF). The machine learning (ML) techniques used in this work have demonstrated strong performance with multiple linear regressions, support vector machines and random forest models with mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE). The investigation has proposed a technique for approximating CO<inf>2</inf> emissions and the results indicate that Support Vector Machine (SVM) can attain the highest degree of precision. The outcome could be a useful model for the decision support system to enhance an appropriate course of action for reducing CO<inf>2</inf> emissions worldwide.