Understanding Mentions of BLS Products Through Topic Modeling of News Articles
Abstract Number:
2192
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Erin Boon (1)
Institutions:
(1) N/A, N/A
First Author:
Presenting Author:
Abstract Text:
The Bureau of Labor Statistics (BLS) measures labor market activity, working conditions, price changes, and productivity in the U.S. economy to support public and private decision making. To meet this mission, BLS not only publishes statistics and research on its own website but also seeks to understand when and where its products are mentioned in online news sources. Making sense of this huge volume of news articles is impossible without a means of summarizing and grouping them. Using article data collected by a third-party service, we experimented with several methods to model the topics contained in news articles that mention BLS products. We compared and optimized candidate models with a goal of meeting the needs of internal stakeholders who use the output to help evaluate the impact of their outreach efforts. Ultimately, we selected a model that provided the best balance of evaluation metrics and utility to these users. This presentation will include a summary of the models we explored and the process we developed to compare them.
Keywords:
topic model|machine learning|natural language processing| | |
Sponsors:
Section on Text Analysis
Tracks:
Miscellaneous
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