%0 Journal Article
	%A Zane Turner and  Kevin Labille and  Susan Gauch
	%D 2020
	%J International Journal of Mechanical and Industrial Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 161, 2020
	%T Lexicon-Based Sentiment Analysis for Stock Movement Prediction
	%U https://publications.waset.org/pdf/10011215
	%V 161
	%X 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.

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