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

TLDR

Extracting sentiment from text is a hard semantic problem. The study develops a methodology to extract small investor sentiment from stock message boards and to assess how management announcements, press releases, news, and regulatory changes influence investor opinion. The method combines several classifiers with a voting scheme. The approach achieves accuracy comparable to Bayes classifiers but with fewer false positives and higher sentiment precision; aggregating messages over time and across stocks improves the sentiment index, especially amid slang, and the index correlates with tech‑sector stock values, trading volumes, and volatility.

Abstract

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.