Multiclass Text Classification of Climate Change Reports: Insights into Natural Disasters, Impacts, Locations, and Time Periods

Abstract

In recent years, the quantity of unstructured information sources, such as documents that include complex information about climate change has grown exponentially. Machine learning techniques may be used to extract semantic information from these unstructured sources to enable semantic organization and classification, as well as a deeper comprehension of the inherent knowledge. Multinomial logistic regression is an effective method for classifying texts into multiple categories simultaneously. Its ability to manage several dependent variables enables precise prediction and categorization, making it ideal for complex climate change data. The paper focuses on the implementation of multinomial logistic regression for text classification to predict the class of a document based on its semantic content. Four custom classifiers were implemented for climate change reports that are tailored to identify and categorize natural disasters and their impacts, locations, and time periods respectively. The ability of the custom classifiers to effectively classify climate change texts was evaluated using an extended report from the Intergovernmental Panel on Climate Change. The resulting predictions from the classifiers highlighted various aspects of the reports regarding these four dimensions and the potential to reveal immanent patterns and relations from unstructured text data.