@article{(Open Science Index):https://publications.waset.org/pdf/10011795, title = {Lexicon-Based Sentiment Analysis for Stock Movement Prediction}, author = {Zane Turner and Kevin Labille and Susan Gauch}, country = {}, institution = {}, abstract = {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. }, journal = {International Journal of Economics and Management Engineering}, volume = {15}, number = {1}, year = {2021}, pages = {149 - 154}, ee = {https://publications.waset.org/pdf/10011795}, url = {https://publications.waset.org/vol/169}, bibsource = {https://publications.waset.org/}, issn = {eISSN: 1307-6892}, publisher = {World Academy of Science, Engineering and Technology}, index = {Open Science Index 169, 2021}, }