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

Department of Finance, Leavey School of Business, Santa Clara University, Santa Clara, California 95053
Ludic Labs, San Mateo, California 94401
srdas{at}scu.edu
mike{at}ludic-lab.com

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 different 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—tech-sector postings are related to stock index levels, and to volumes and volatility. The algorithms may be used to assess the impact on investor opinion of management announcements, press releases, third-party news, and regulatory changes.

Key Words: text classification; index formation; computers-computer science; artificial intelligence; finance; investment
History: Received: May 4, 2004;

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