Analysis and tuning of hierarchical topic models based on Renyi entropy approach

Uncovering lobbying strategies in sustainable finance disclosure regulations using machine learning

PURPOSE

We analyse lobbying behaviour by using Machine Learning approaches. In the context of Sustainable Finance Disclosure Regulation (SFDR), we gain detailed insights, assign these to existing strategies, and measure how strongly which participant influences the regulation.

STUDY DESIGN/METHODOLOGY/APPROACH

We use tri-gram analysis, sentiment analysis, and similarity analysis as methods to obtain insights into the political commentary process of European Supervisory Authorities (ESAs) drafts dealing with SFDR.

FINDINGS

Our metadata helps to identify stakeholders and lobbying strategies. We found that the most negative comments came from the regulated, who argued strongly subjectively in a very objective environment of ESG disclosure. We also identified typical lobbying strategies based on arguments, persuasion, and classic cost-benefit considerations.

ORIGINALITY/VALUE

We generate emotion values and synthesise detailed argument differences and show that modern algorithms can contribute to the identification of interest groups and lobbying strategies. Furthermore, we generate similarity values of arguments that can be taken into account in the analysis of the success of a lobbying strategy.

Topic models with elements of neural networks: investigation of stability, coherence, and determining the optimal number of topics

Topic modeling is a widely used instrument for the analysis of large text collections. In the last few years, neural topic models and models with word embeddings have been proposed to increase the quality of topic solutions. However, these models were not extensively tested in terms of stability and interpretability. Moreover, the question of selecting the number of topics (a model parameter) remains a challenging task. We aim to partially fill this gap by testing four well-known and available to a wide range of users topic models such as the embedded topic model (ETM), Gaussian Softmax distribution model (GSM), Wasserstein autoencoders with Dirichlet prior (W-LDA), and Wasserstein autoencoders with Gaussian Mixture prior (WTM-GMM). We demonstrate that W-LDA, WTM-GMM, and GSM possess poor stability that complicates their application in practice. ETM model with additionally trained embeddings demonstrates high coherence and rather good stability for large datasets, but the question of the number of topics remains unsolved for this model. We also propose a new topic model based on granulated sampling with word embeddings (GLDAW), demonstrating the highest stability and good coherence compared to other considered models. Moreover, the optimal number of topics in a dataset can be determined for this model.

Optimized Multiscale Entropy Model Based on Resting-State fMRI for Appraising Cognitive Performance in Healthy Elderly

Many studies have indicated that an entropy model can capture the dynamic characteristics of resting-state functional magnetic resonance imaging (rfMRI) signals. However, there are problems of subjectivity and lack of uniform standards in the selection of model parameters relying on experience when using the entropy model to analyze rfMRI. To address this issue, an optimized multiscale entropy (MSE) model was proposed to confirm the parameters objectively. All healthy elderly volunteers were divided into two groups, namely, excellent and poor, by the scores estimated through traditional scale tests before the rfMRI scan. The parameters of the MSE model were optimized with the help of sensitivity parameters such as receiver operating characteristic (ROC) and area under the ROC curve (AUC) in a comparison study between the two groups. The brain regions with significant differences in entropy values were considered biomarkers. Their entropy values were regarded as feature vectors to use as input for the probabilistic neural network in the classification of cognitive scores. Classification accuracy of 80.05% was obtained using machine learning. These results show that the optimized MSE model can accurately select the brain regions sensitive to cognitive performance and objectively select fixed parameters for MSE. This work was expected to provide the basis for entropy to test the cognitive scores of the healthy elderly.