Paper: Identifying and Following Expert Investors in Stock Microblogs

ACL ID D11-1121
Title Identifying and Following Expert Investors in Stock Microblogs
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2011
Authors

Information published in online stock invest- ment message boards, and more recently in stock microblogs, is considered highly valu- able by many investors. Previous work fo- cused on aggregation of sentiment from all users. However, in this work we show that it is beneficial to distinguish expert users from non-experts. We propose a general framework for identifying expert investors, and use it as a basis for several models that predict stock rise from stock microblogging messages (stock tweets). In particular, we present two methods thatcombineexpertidentificationandper-user unsupervised learning. These methods were shown to achieve relatively high precision in predicting stock rise, and significantly outper- form our baseline. In addition, our work pro- vides an in-depth analysis of the...