Unlock the Stock: User Topic Modeling for Stock Market Analysis
The increasing use of Twitter as a medium for sharing news related to various topics, facilitates methods for automatic news creation or event detection and prediction. However, these methods are hindered by users posting and propagating incorrect or irrelevant content. Choosing the right users is crucial in order to sample down the tweets to be analyzed, and preserve the quality of the predicted events or generated news. In this paper, we present an e ective method for identifying expert users in defined areas related to the stock market. For each user we generate a model based on the content of their posts. The model represents the domains the user talks about, and allows a selection of users for various tasks. We show the e ectiveness of the proposed approach by performing a series of experiments using large Twitter datasets related to Stock Market Companies.
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