Twitter data transformation for network visualization based context analysis

Graph visualization is often used for a representation of interconnected relations of entities. Individual entities such as cells, humans, computers, users, and other entities are represented as vertices while edges are used for entities' relations. With the visualization, people can get more information from the graph. There are already many tools for graph visualization of social media data. However, those tools need a specific file input format before generating a graph and visualizing it. In this research, we tried to do experiment and analysed data transformation from Twitter raw data to a JSON file format that can be used for most of the web-based network visualization tools. Using those techniques, we can decrease the memory usage by 1,526.1 MB and increase graph formation running time by 1,718.75 seconds. Those techniques can also be added with Text Mining modules for online context analysis of network visualization.