NEWS DATA VISUALIZATION INTERFACE DEVELOPMENT USING NMF ALGORITHM

News data is a super large-scale dataset. It covers a wide range of topics ranging from heavy topics such as politics and society to beauty and entertainment, relatively light topics. At the same time, it is also the most accessible source of information for the general public to obtain information. Thus, how is this large amount of data used by the general public being utilized? Currently, services provided by news platforms are just full article searches and related news recommendations. It uses only a fraction of the vast news dataset, and there is still a lack of systems to fully utilize and analyze it. As mentioned above, news datasets which contain a wide range of topics and super large scales of data, record everything that happened in the past and present, so analyzing and visualizing them can track how trends in real-world change over time and even discover what the topics of the large dataset are without reading the full text through topic modeling. For this objective, in this thesis, we propose a novel interactive visualization interface for the news data based on NMF to analyze, visualize, and utilize datasets more practically than simply searching the articles. Through this thesis, We first show the superior topic modeling performance of the NMF algorithm and the superior processing speed that can be used for interactive visual interface compared to other methods and then suggest the visual interface that contains various features to help users better analyze and intuitively understand the data. Finally, we present use cases on how this study can be used practically and present their applicability in various fieldsM.S