Date of Graduation

12-2019

Document Type

Thesis

Degree Name

Bachelor of Science

Degree Level

Undergraduate

Department

Computer Science and Computer Engineering

Advisor/Mentor

Zane

Committee Member/Reader

Gauch, John

Committee Member/Second Reader

Parkerson, James

Committee Member/Third Reader

Gauch, Susan

Abstract

Sentiment analysis is a broad and expanding field that aims to extract and classifying opinions from textual data. Lexicon-based approaches are based on using a sentiment lexicon, a list of words each mapped to a sentiment score, to rate the sentiment of a text chunk. Our work focuses on predicting stock price change using a sentiment lexicon built from financial conference call logs. We introduce a method to generate a sentiment lexicon based upon an existing probabilistic approach. By using a domain-specific lexicon, we outperform traditional techniques and demonstrate that domain-specific sentiment lexicons provide higher accuracy than generic sentiment lexicons when predicting stock price change.

Keywords

Stock price prediction, sentiment analysis, domain-specific lexicon, lexicon

Available for download on Monday, December 05, 2022

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