Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web

Extracting sentiment from text is a hard semantic problem. We develop a methodology for extracting small investor sentiment from stock message boards. The algorithm comprises dierent classifier algorithms coupled together by a voting scheme. Accuracy levels are similar to widely used Bayes classifiers, but false positives are lower and sentiment accuracy higher. Time series and cross-sectional aggregation of message information improves the quality of the resultant sentiment index, particularly in the presence of slang and ambiguity. Empirical applications evidence a relationship with stock values – aggregate tech sector sentiment is found to predict stock index levels, but not at the individual stock level. The algorithms may be used to assess the impact on investor opinion of management announcements, press releases, third-party news, and regulatory changes.