相关实验视频
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Using Generative Art to Convey Past and Future Climate Transitions
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在社交媒体和博客上探索气候变化话语,使用主题建模分析分析
Tunahan Gokcimen1, Bihter Das1
1Department of Software Engineering, Technology Faculty, Firat University, 23119, Elazig, Turkey.
Heliyon
|July 1, 2024
概括
分析社交媒体和博客揭示了公众对气候变化的看法. 基于OpenAI的BERTopic有效地模拟了这些话题,为气候政策和减缓战略提供了信息.
科学领域:
- 环境科学 环境科学
- 计算社会科学 计算社会科学
背景情况:
- 了解公众对气候变化的看法对于有效的减缓政策至关重要.
- 传统的调查有局限性;需要新的方法来分析在线话语.
研究的目的:
- 用社交媒体和博客数据分析公众对气候变化的态度.
- 为了比较关键字提取和文本表示技术用于主题建模.
- 通过数据分析识别气候变化关键话题和公众情绪.
主要方法:
- 收集了来自社交媒体和博客的气候变化相关文本数据集.
- 应用BERTopic和隐藏的迪里克莱特分配 (LDA) 用于主题识别和可视化.
- 利用句子相似性来分析评论分类和关系.
- 评估了关键词提取方法,包括OpenAI,最大边际相关性 (MMR) 和KeyBERT与BERTopic.
主要成果:
- 基于OpenAI的BERTopic在连贯性和主题多样性方面表现出卓越的表现.
- 从在线话语中确定了与气候变化有关的关键主题和公众看法.
- 句子相似性分析为主题类别中的评论聚类提供了见解.
结论:
- 基于OpenAI的BERTopic是一种高效的方法,用于从在线文本中分析公众对气候变化的看法.
- 这些发现为决策者提供了有价值的见解,旨在制定有效的气候变化缓解战略.
- 利用用户生成的在线内容提供了一种可扩展的方法来了解公众对关键环境问题的看法.
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