The competition furnished a meaningful opportunity for team members to strengthen their skills, particularly working with natural language processing (NLP), and begin exploring national security applications for their work. “Before this, I had zero background in NLP," said Nathan, who, like his colleagues, has primarily supported clients in LMI’s defense and health/civilian markets. “It’s been very helpful diving into [NLP tools]. I learned a lot about how these tools can be applied in the national security space and beyond.”
The month-long development process started with All the News, a publicly available dataset containing articles from 15 popular English newspapers from 2016 to 2017. Article texts and metadata were extracted, organized, and cleaned in the Python programming language. Sentiments were then calculated for each article using sentiment analysis libraries, including VADER (Valence Aware Dictionary and sEntiment Reasoner), StanfordNLP, and TextBlob. The negative, positive and neutral article counts for each publication source were aggregated by month and visualized as a line graph.
“We are incredibly proud of this team, one of the few in the competition not to borrow from an existing proprietary technology. The platform was designed and constructed from the ground up, burnishing LMI’s reputation as a leader in innovation,” said Brant Horio, director of data science. “The team’s submission deserved this recognition, which helps strategically position LMI for future national security work.”