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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 outperform similar current textual indicators in some situations, particularly in the financial market.

Supplementary information

The online version contains supplementary material available at 10.1007/s43546-022-00209-2.

SUBMITTER: Wang MD

PROVIDER: S-EPMC9028908 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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

Wang Michael D MD Lou Jie J Zhang Dong D Fan C Simon CS

SN business & economics 20220422 5

In this paper, we develop a computational linguistic approach based on supervised machine learning using the <i>People's Daily</i> 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 com ...[more]

PMID: 35493720

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Project description:BackgroundImbalances in approach-avoidance conflict (AAC) decision-making (e.g., sacrificing rewards to avoid negative outcomes) are considered central to multiple psychiatric disorders. We used computational modelling to examine 2 factors that are often not distinguished in descriptive analyses of AAC: decision uncertainty and sensitivity to negative outcomes versus rewards (emotional conflict).MethodsA previously validated AAC task was completed by 478 participants, including healthy controls (n = 59), people with substance use disorders (n = 159) and people with depression and/or anxiety disorders who did not have substance use disorders (n = 260). Using an active inference model, we estimated individual-level values for a model parameter that reflected decision uncertainty and another that reflected emotional conflict. We also repeated analyses in a subsample (59 healthy controls, 161 people with depression and/or anxiety disorders, 56 people with substance use disorders) that was propensity-matched for age and general intelligence.ResultsThe model showed high accuracy (72%). As further validation, parameters correlated with reaction times and self-reported task motivations in expected directions. The emotional conflict parameter further correlated with self-reported anxiety during the task (r = 0.32, p < 0.001), and the decision uncertainty parameter correlated with self-reported difficulty making decisions (r = 0.45, p < 0.001). Compared to healthy controls, people with depression and/or anxiety disorders and people with substance use disorders showed higher decision uncertainty in the propensity-matched sample (t = 2.16, p = 0.03, and t = 2.88, p = 0.005, respectively), with analogous results in the full sample; people with substance use disorders also showed lower emotional conflict in the full sample (t = 3.17, p = 0.002).LimitationsThis study was limited by heterogeneity of the clinical sample and an inability to examine learning.ConclusionThese results suggest that reduced confidence in how to act, rather than increased emotional conflict, may explain maladaptive approach-avoidance behaviours in people with psychiatric disorders.

Dataset Information

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

Supplementary information

Publications

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

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