Measuring political and economic uncertainty: a supervised computational linguistic approach.

ABSTRACT: In this paper, we develop a computational linguistic approach based on supervised machine learning using the People's Daily to measure Chinese official relations and political uncertainty towards the US. In the first step, we create training samples by asking experts to manually annotate news articles. In the second step, we use supervised machine learning algorithms to adjust our single neural network and support vector machine classifiers to better fit our training data. Finally, we combine our two individual classifiers and a dictionary approach to automatically detect whether an article in the newspaper sample is relevant. Using all of the relevant textual data, we then apply the computational linguistic approach to generate state-of-the-art indices and show that our indices out

SUBMITTER: Wang MD 

PROVIDER: S-EPMC9028908 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature