Search

This research aimed to analyze drug-related news articles from 2021 to 2023 using topic modeling techniques to understand the main themes and trends during this period. The article data were collected from the news data analysis system, Big Kinds. The research process included data preprocessing, Latent Dirichlet Allocation (LDA) topic modeling, and interpretation of the results. After conducting topic modeling and visualization, the analysis of the topics revealed that there was not a significant difference in the themes of drug-related articles between 2021 and 2022. However, in 2023, new topics emerged, such as the explosive increase in articles related to drug offenders and the prevention of drug use among adolescents. [ABSTRACT FROM AUTHOR]

Natural Language Processing is one of the fields that attracts a lot of attention in deep learning, and with the introduction of transformer-based GPT[1] and BERT[2], it is showing tremendous performance improvement. In this paper, we compared and analyzed the performance of word embedding, neural network, and pre-trained language model, dependent othe model and data type of the news. ISOT, Kaggle, and Politifact datasets were used for fake news dataset as a result, BERT showed best performance in this study, however in Politifact Dataset, it showed relatively poor performance. We analyzed the structure of dataset and from the model perspectives to find out the reason why the performance differences were occurred. [ABSTRACT FROM AUTHOR]

The news articles collected with keyword “non faceto-face” were analyzed through topic modeling applied with LDA algorithm. In this study, collected articles were divided into two periods, period 1(the beginning of COVID-19 spread) and period 2(the end of COVID-19 spread), according to issued date of the articles. The articles of period 1 showed support for non-faceto-face treatment, smart library, the beginning of the online financial era, non-face-to-face entrance exam and employment, stock investment for main topic words. And the articles of period 2 showed conversion to non face-to-face classes, increasing unmanned stores, online finance, education industry, home treatment for main topic words. Also, further issues were discussed through visualization of topic words. These results provide evidence that education and unmanned business in non-face-to-face industries are growing. [ABSTRACT FROM AUTHOR]