@article{(Open Science Index):https://publications.waset.org/pdf/10011215,
	  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 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.
	    journal   = {International Journal of Mechanical and Industrial Engineering},
	  volume    = {14},
	  number    = {5},
	  year      = {2020},
	  pages     = {185 - 191},
	  ee        = {https://publications.waset.org/pdf/10011215},
	  url   	= {https://publications.waset.org/vol/161},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 161, 2020},