Dissertations, Theses, and Capstone Projects

Date of Degree


Document Type


Degree Name





Kyle Gorman

Subject Categories

Computational Linguistics


Natural Language Processing, Computational Linguistics


In a quick search online, one can find many tools which use information from news headlines to make predictions concerning the trajectory of a given stock. But what if we went further, looking instead into the text of the article, to extract this and other information? Here, the goal is to extract the sentence in which a stock ticker symbol is mentioned from a news article, then determine sentiment and subjectivity values from that sentence, and finally make a prediction on whether or not the value of that stock will go up or not in a 24-hour timespan. Bloomberg News articles published between 2008 and 2013 were used as a data source, and prices of stocks were acquired using Yahoo Finance. News and information influence human behavior; constantly changing, the effects of this information on the market can be observed daily. This technology could assist people in making better decisions on how to invest their money.

CL_MA_Thesis_Code_AK.zip (621 kB)
Code Repository