Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web
提出一种从股票论坛帖子中提取散户投资者情绪的方法，通过组合分类器与投票机制降低误报、提高情感准确度，并发现科技板块帖子与股指、成交量及波动性相关。
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.