Date of Award:

12-2018

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Computer Science

Advisor/Chair:

Nicholas Flann

Co-Advisor/Chair:

Vladimir Kulyukin

Third Advisor:

Xiaojun Qi

Abstract

The aim of this thesis is to study and identify time periods of high activity in commodity and stock market sentiment based on a data mining approach. The method is to develop tools to extract relevant information from web searches and Twitter feeds based on the tally of certain keywords and their combinations at regular intervals. Periods of high activity are identified by a measure of complexity developed for analysis of living systems. Experiments were conducted to see if the measure of activity could be applied as a predictor of changes in stock market and commodity prices.

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Available for download on Friday, December 01, 2023

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