Lexicon-based sentiment analysis for stock movement prediction

Sentiment analysis is a broad and expanding field that aims to extract and classify opinions from textual data. Lexiconbased 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 domainspecific sentiment lexicons provide higher accuracy than generic sentiment lexicons when predicting stock price change. Keywords—Lexicon, sentiment analysis, stock movement prediction., computational finance