Visualizing Trends of Key Roles in News Articles
Chen Xia, Haoxiang Zhang, Jacob Moghtader, Allen Wu, Kai-Wei Chang
TL;DR
This paper introduces a visualization system that tracks and displays the evolving trends of key roles in news articles using NLP techniques, aiding understanding of news dynamics over time.
Contribution
It presents a novel system combining semantic role labeling and dynamic word embeddings to visualize key role trends in news articles over time.
Findings
Effective visualization of key role trends
Insights into news topic evolution
Demonstration of NLP techniques in trend analysis
Abstract
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.
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Taxonomy
TopicsAdvanced Text Analysis Techniques · Web Data Mining and Analysis · Topic Modeling