Stock Price Using Domain Specific Lexicons

Sentiment analysis is a broad and expanding field that aims to extract and classifying opinions from textual data. Lexicon-based approaches are based on using a sentiment lexicon, 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