Paper: Exploiting Social Relations and Sentiment for Stock Prediction

ACL ID D14-1120
Title Exploiting Social Relations and Sentiment for Stock Prediction
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

In this paper we first exploit cash-tags (?$? fol- lowed by stocks? ticker symbols) in Twitter to build a stock network, where nodes are stocks connected by edges when two stocks co-occur frequently in tweets. We then employ a labeled topic model to jointly model both the tweets and the network structure to assign each node and each edge a topic respectively. This Semantic Stock Network (SSN) summarizes discussion topics about stocks and stock relations. We fur- ther show that social sentiment about stock (node) topics and stock relationship (edge) topics are predictive of each stock?s market. For predic- tion, we propose to regress the topic-sentiment time-series and the stock?s price time series. Ex- perimental results demonstrate that topic senti- ments from close neighbors a...