19c96fd5174bda1ab181872 20210206011440553 iocm farid.sartipi@iconsmat.com.au WEB-FORM Journal of Construction Materials JCM 26523752 10.36756/JCM https://iconsmat.com.au/publication/ 4 2 2021 2 3 10.36756/JCM.v2.3 https://iconsmat.com.au/v-2-3/ Computer Science and Computer Engineering, University of Arkansas, Fayetteville, Arkansas, United States Zane Turner Kevin Labille Computer Science and Computer Engineering, University of Arkansas, Fayetteville, Arkansas, United States Susan Gauch Computer Science and Computer Engineering, University of Arkansas, Fayetteville, Arkansas, United States Sentiment analysis is a broad and expanding field that aims to extract and classify opinions from textual data. Lexicon-based approaches are based on the use of a sentiment lexicon, i.e., 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 present 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. 4 2 2021 10.36756/JCM.v2.3.5 https://iconsmat.com.au/wp-content/uploads/2021/02/v2.3.5.pdf