Pattern-Based Opinion Mining for Stock Market Trend Prediction

Abstract

Stock market reports in on-line news are widely used by amateurs to make quick investment decisions. Financial analysts often give opinions about trends of stock markets based on past and present economic event indicators. These opinions commonly appear in text form and are abundant over the Internet. It is tedious and time consuming for users to browse through such text manually let alone to understand the embedded opinions. To overcome this shortcoming, automatic trend predication methods have been proposed. Under conventional methods, reports are represented using bag of words and trend prediction is treated as a 3-way trend classification problem, i.e. trend as 'up', 'down' or 'stable'. In this paper, we propose a new pattern-based opinion mining method for market trend predication. Experiments show that (1) pattern-based classification is more effective than its word-based counterpart for feature representation; and (2) opinion mining outperforms event-based classification for trend predication. The task of opinion mining gets more difficult when the users are exposed to opinions from more than one analyst. The question becomes whose opinions should he/she trust? This lays down our second research objective, i.e. to study different opinion incorporation strategies. Intuitively, one would trust the opinion supported by the majority. However, we show that on the contrary, the user is better off trusting the most credible analyst.