Text Classification of Climate Change Tweets using Artificial Neural Networks, FastText Word Embeddings, and Latent Dirichlet Allocation

John Daves S. Baguio, Billy A. Lu, Christine F. Peña
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引用次数: 0

Abstract

The climate change discourse on social media happens rapidly with microblogging sites such as Twitter. On these types of sites, there is a divide of stances. Some people believe that climate change is man-made, and some people deny its existence. This study aimed to classify climate change tweets in the given labeled dataset with the created text classification model that used Artificial Neural Networks, FastText Word Embeddings, and Latent Dirichlet Allocation. Additionally, domain-specific preprocessing methods for climate change tweets and adding features by appending the majority topic of a given tweet between each word are applied. This study has shown that the created text classification model improved the F1 score of the two undersampled classes by 1 % and 6 % respectively while still maintaining a good F1 score for the majority class. The text classification model overall increased both macro and weighted averages by 3 % and 1 % respectively.
基于人工神经网络、快速文本词嵌入和潜在狄利克雷分配的气候变化推文文本分类
社交媒体上关于气候变化的讨论在Twitter等微博网站上迅速展开。在这些类型的网站上,有不同的立场。有些人认为气候变化是人为造成的,而有些人则否认它的存在。本研究旨在使用人工神经网络、快速文本词嵌入和潜在狄利克雷分配所创建的文本分类模型,对给定标记数据集中的气候变化推文进行分类。此外,还应用了针对气候变化tweet的特定领域预处理方法,并通过在每个单词之间附加给定tweet的主要主题来添加特征。本研究表明,所创建的文本分类模型将两个欠采样类的F1分数分别提高了1%和6%,同时对于大多数类仍然保持良好的F1分数。文本分类模型总体上使宏观平均值和加权平均值分别提高了3%和1%。
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