Visualizing Trends of Key Roles in News Articles
This work addresses the need for trend analysis in news for researchers or analysts, but it is incremental as it applies existing methods to a specific domain.
The paper tackled the problem of analyzing key roles in news articles by developing a demonstration system that visualizes trends using natural language processing techniques, resulting in a tool that tracks changes in key roles and news topics over time.
There are tons of news articles generated every day reflecting the activities of key roles such as people, organizations and political parties. Analyzing these key roles allows us to understand the trends in news. In this paper, we present a demonstration system that visualizes the trend of key roles in news articles based on natural language processing techniques. Specifically, we apply a semantic role labeler and the dynamic word embedding technique to understand relationships between key roles in the news across different time periods and visualize the trends of key role and news topics change over time.