Matthias Murray is studying for a Masters in Data Science at New College of Florida. He previously studied a BA in Maths and Physics also at New College of Florida, producing a thesis on Thin Film Fracture and Finite Element Analysis Fundamentals.
The length and technical detail of SEC filings makes them largely inaccessible for most investors to read or analyze, and with the growing volume of data, these filings are getting longer and more numerous. It is therefore increasingly of interest to seek automation of part of this task using natural language processing (NLP).
Matthias's poster compares and contrasts approaches to sentiment analysis to address this problem and evaluate the efficacy of NLP for such a task.
In this Video Recording, Matthias provides a tour and explanation of his poster content.
Applying HPCC Systems
TextVector to SEC Filings
Click on the poster for a larger image. The original PDF version can be found here. (Available for download).