Building Customized Text Mining Tools via Shiny Framework: The Future of Data Visualization

摘要

With the increasing volume of data, there is a growing need for dynamic data visualization to help reveal instant changes in data patterns. There exist many commercial visualization tools, but traditional scholars are often disengaged from the tool development process; thus, the choice of functionalities is contingent upon tool developers whose choice may not fit the end-users. This collaboration, however, has a potential in bridging the gap between traditional scholars, who are more interested in sense-making of the text than in the tools, and the data scientists, who are more interested in the tools than in the substance, but must still contextualize the outcomes. Until recently, this collaborative process was hindered by the complexity of customization procedures and technological hurdles imposed on users with new installations. With the advent of reactive web frameworks, such as Shiny, the user-driven customization becomes not only feasible, but also essential to advance scientific research. In this paper, we demonstrate a collaborative effort between learned scholars and tool developers, allowing for a computational and humanistic fusion.