Story Analysis Using Natural Language Processing and Interactive Dashboards

ABSTRACT

This paper discusses Story Analyzer, which uses a natural language processing (NLP) library and sophisticated data visualization libraries to produce dashboards of interrelated and user-responsive visualizations depicting actors and their interactions in a textual narrative, along with locations, times, and other contexts. Story Analyzer performs information extraction using Stanford’s CoreNLP’s NLP services including sentence recognition, tokenizing, parts-of-speech identification, dependency parsing, named entity recognition, coreference resolution, and temporal tagging. Visualization is done through D3 and scalable vector graphics (SVG) which provide powerful control over gelements and shapes in browser-based user interfaces. Google Charts and Maps are also used for visualizations. Development using NLP for unstructured textual data involves challenges, limitations, and ambiguities that distinguishes it from applications using structured data. Therefore, the paper also discusses issues and limitations inherent with using NLP libraries, and presents workarounds when applied to story analysis. Story Analyzer is applied to a contemporary news article regarding data privacy issues.

KEYWORDS:

• Natural language processing (NLP)
• data visualization
• story analysis
• NLP accuracy issues and mitigations
• story dashboard generation

Acknowledgments

Thanks go to James Madison University’s College of Business, who supported an educational leave in Fall 2015, during which this research began.