Detecting and Analyzing Politically-Themed Stocks Using Text Mining Techniques and Transfer Entropy—Focus on the Republic of Korea’s Case

Choi, Ik-Hyeon; Kim, Woo Chang

doi:10.3390/e23060734

Cited by 9 publications

(4 citation statements)

References 46 publications

(91 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Final modifications were made to some heterogeneous food names in clusters using the designated cluster number of food Many studies have applied LDA to topic modeling methods in various fields, such as keyword selection, source code analysis, opinion mining, event detection, music key profiling, image classification, a recommender system, sentiment classification, and even political issues (13).…”

Section: Data Preprocessingmentioning

confidence: 99%

Dietary Pattern Extraction Using Natural Language Processing Techniques

Choi

Kim

2022

Front. Nutr.

Self Cite

View full text Add to dashboard Cite

In this study, we observed the changes in dietary patterns among Korean adults in the previous decade. We evaluated dietary intake using 24-h recall data from the fourth (2007–2009) and seventh (2016–2018) Korea National Health and Nutrition Examination Survey. Machine learning-based methodologies were used to extract these dietary patterns. Particularly, we observed three dietary patterns from each survey similar to the traditional and Western dietary patterns in 2007–2009 and 2016–2018, respectively. Our results reveal a considerable increase in the number of Western dietary patterns compared with the previous decade. Thus, our study contributes to the use of novel methods using natural language processing (NLP) techniques for dietary pattern extraction to obtain more useful dietary information, unlike the traditional methodology.

show abstract

Section: Data Preprocessingmentioning

confidence: 99%

Dietary Pattern Extraction Using Natural Language Processing Techniques

Choi

Kim

2022

Front. Nutr.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Our deployment of NLP, mainly through the LDA algorithm, which is used in various areas [ 42 ], marks a significant stride in understanding the nuanced architecture of dietary habits beyond the conventional scope identified in related previous works on dietary patterns [ 43 , 44 , 45 , 46 , 47 ]. This advanced analysis permits a dive into the complex mosaic of food intake and preferences without the constraints of pre-established dietary categorizations.…”

Section: Discussionmentioning

confidence: 99%

An Explainable Prediction for Dietary-Related Diseases via Language Models

Choi,

Kim,

Kim

2024

Nutrients

Self Cite

View full text Add to dashboard Cite

Our study harnesses the power of natural language processing (NLP) to explore the relationship between dietary patterns and metabolic health outcomes among Korean adults using data from the Seventh Korea National Health and Nutrition Examination Survey (KNHANES VII). Using Latent Dirichlet Allocation (LDA) analysis, we identified three distinct dietary patterns: “Traditional and Staple”, “Communal and Festive”, and “Westernized and Convenience-Oriented”. These patterns reflect the diversity of dietary preferences in Korea and reveal the cultural and social dimensions influencing eating habits and their potential implications for public health, particularly concerning obesity and metabolic disorders. Integrating NLP-based indices, including sentiment scores and the identified dietary patterns, into our predictive models significantly enhanced the accuracy of obesity and dyslipidemia predictions. This improvement was consistent across various machine learning techniques—XGBoost, LightGBM, and CatBoost—demonstrating the efficacy of NLP methodologies in refining disease prediction models. Our findings underscore the critical role of dietary patterns as indicators of metabolic diseases. The successful application of NLP techniques offers a novel approach to public health and nutritional epidemiology, providing a deeper understanding of the diet–disease nexus. This study contributes to the evolving field of personalized nutrition and emphasizes the potential of leveraging advanced computational tools to inform targeted nutritional interventions and public health strategies aimed at mitigating the prevalence of metabolic disorders in the Korean population.

show abstract

“…Transfer entropy quantifies the causal relationship in the system based on measuring the resolved uncertainty according to the flow of information and the magnitude of the causal relationship from the source to the target variable. Transfer entropy has been used in the financial sector to confirm the causal relationship between financial assets and financial markets in various studies (Marschinski & Kantz, 2002 ; Kwon & Yang, 2008 ; Dimpfl & Peter, 2013 ; Sensoy et al, 2014 ; Bekiros et al, 2017 ; Lim et al, 2017 ; Jang et al, 2019 ; Yue et al, 2020a , b ; Choi & Kim, 2021 ). Based on the prior studies, this study chose transfer entropy to analyze exchange rates and exchange rates risk fluctuations.…”

Section: Methodsmentioning

confidence: 99%

Improving data efficiency for analyzing global exchange rate fluctuations based on nonlinear causal network-based clustering

2022

Self Cite

View full text Add to dashboard Cite

This study used information theory and network theory to predict the fluctuations of currency values of the machine learning model. For experiments, we calculate the causal relationships between currencies using loarithmic return (log-return) and entropic value-at-risk (EVaR) values of gold price per troy ounce in 48 currencies over 25 years. To quantify the causal relationships, we used the concept of transfer entropy. After quantifying their information flow, we modeled and analyzed those nonlinear causal relationships as a network. The network analysis results confirmed that information flow-based nonlinear causal relationships differed from the commonly-known key currency order. Then, we classified currencies using hierarchical clustering methods based on the configured networks. We predicted fluctuations in currency values using machine learning algorithms based on network topology-based information. As a result, we show that using the data columns in the same communities based on statistically significant nonlinear causal relationships can improve most machine-learning-based fluctuations of currency values for various countries from the perspective of data efficiency.

show abstract

Detecting and Analyzing Politically-Themed Stocks Using Text Mining Techniques and Transfer Entropy—Focus on the Republic of Korea’s Case

Cited by 9 publications

References 46 publications

Dietary Pattern Extraction Using Natural Language Processing Techniques

Dietary Pattern Extraction Using Natural Language Processing Techniques

An Explainable Prediction for Dietary-Related Diseases via Language Models

Improving data efficiency for analyzing global exchange rate fluctuations based on nonlinear causal network-based clustering

Contact Info

Product

Resources

About