Gokcimen Tunahan, Das Bihter
Department of Software Engineering, Technology Faculty, Firat University, 23119, Elazig, Turkey.
Heliyon. 2024 Jun 5;10(11):e32464. doi: 10.1016/j.heliyon.2024.e32464. eCollection 2024 Jun 15.
Climate change is one of the most pressing global issues of our time, and understanding public perception and awareness of the topic is crucial for developing effective policies to mitigate its effects. While traditional survey methods have been used to gauge public opinion, advances in natural language processing (NLP) and data visualization techniques offer new opportunities to analyze user-generated content from social media and blog posts. In this study, a new dataset of climate change-related texts was collected from social media sources and various blogs. The dataset was analyzed using BERTopic and LDA to identify and visualize the most important topics related to climate change. The study also used sentence similarity to determine the similarities in the comments written and which topic categories they belonged to. The performance of different techniques for keyword extraction and text representation, including OpenAI, Maximal Marginal Relevance (MMR), and KeyBERT, was compared for topic modeling with BERTopic. It was seen that the best coherence score and topic diversity metric were obtained with OpenAI-based BERTopic. The results provide insights into the public's attitudes and perceptions towards climate change, which can inform policy development and contribute to efforts to reduce activities that cause climate change.
气候变化是我们这个时代最紧迫的全球问题之一，了解公众对该话题的认知和意识对于制定有效的政策以减轻其影响至关重要。虽然传统的调查方法已被用于衡量公众意见，但自然语言处理（NLP）和数据可视化技术的进步为分析来自社交媒体和博客文章的用户生成内容提供了新机会。在本研究中，从社交媒体来源和各种博客收集了一个与气候变化相关的文本新数据集。使用BERTopic和LDA对该数据集进行分析，以识别和可视化与气候变化相关的最重要主题。该研究还使用句子相似度来确定所写评论中的相似之处以及它们所属的主题类别。将包括OpenAI、最大边际相关性（MMR）和KeyBERT在内的不同关键词提取和文本表示技术在与BERTopic进行主题建模时的性能进行了比较。结果表明，基于OpenAI的BERTopic获得了最佳的连贯分数和主题多样性指标。这些结果提供了对公众对气候变化态度和认知的见解，可为政策制定提供参考，并有助于减少导致气候变化活动的努力。
