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
Citation
Turner, Z. (2019). Stock Price Using Domain Specific Lexicons. Computer Science and Computer Engineering Undergraduate Honors Theses Retrieved from https://scholarworks.uark.edu/csceuht/72