Topic modeling and sentiment analysis of global climate change tweets
Topic modeling and sentiment analysis of global climate change tweets
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DOI：
10.1007/s13278-019-0568-8
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发表时间：
2019-06-10
影响因子：
2.8
通讯作者：
Li, Zhenlong
中科院分区：
文献类型：
--
作者：
Dahal, Biraj;Kumar, Sathish A. P.;Li, Zhenlong
社交媒体网站可以被用作挖掘包括气候变化在内的各种话题的公众舆论的数据源。尤其是Twitter，允许对跨越时间和空间的民意进行评估，因为地理标记的推文包括时间戳和地理坐标(纬度/经度)。在这项研究中，使用卷分析和文本挖掘技术(如主题建模和情感分析)分析了包含某些与气候变化相关的关键字的地理标记推文的大型数据集。潜在狄利克雷分配用于主题建模以推断讨论的不同主题，配价感知词典和情感推理器用于情感分析以确定在数据集中发现的总体感觉和态度。这些技术被用来比较和对比不同国家之间和一段时间内气候变化讨论的性质。情绪分析显示，总体讨论是负面的，特别是当用户对政治或极端天气事件做出反应时。主题建模表明，关于气候变化的不同讨论主题是多样的，但一些主题比其他主题更普遍。特别是，与其他国家相比，美国对气候变化的讨论较少关注与政策相关的话题。
Social media websites can be used as a data source for mining public opinion on a variety of subjects including climate change. Twitter, in particular, allows for the evaluation of public opinion across both time and space because geotagged tweets include timestamps and geographic coordinates (latitude/longitude). In this study, a large dataset of geotagged tweets containing certain keywords relating to climate change is analyzed using volume analysis and text mining techniques such as topic modeling and sentiment analysis. Latent Dirichlet allocation was applied for topic modeling to infer the different topics of discussion, and Valence Aware Dictionary and sEntiment Reasoner was applied for sentiment analysis to determine the overall feelings and attitudes found in the dataset. These techniques are used to compare and contrast the nature of climate change discussion between different countries and over time. Sentiment analysis shows that the overall discussion is negative, especially when users are reacting to political or extreme weather events. Topic modeling shows that the different topics of discussion on climate change are diverse, but some topics are more prevalent than others. In particular, the discussion of climate change in the USA is less focused on policy-related topics than other countries.