Text Mining Economic Topic Sentiment for Time Series Modeling
Michael P. Dessauer, Justin Kauhl · 2014
Global businesses must react to daily changes in market conditions over multiple geographies and industries. Consuming reputable daily economic reports assists in understanding these changing conditions, but requires both a significant human time commitment and a subjective assessment of each topic area of interest. To combat these constraints, Dow's Advanced Analytics team has constructed a process to calculate sentence-level topic frequency and sentiment scoring from unstructured economic reports. Daily topic sentiment scores are aggregated to weekly and monthly intervals and used as exogenous variables to model external economic time series data. These models serve to both validate the relationship between our sentiment scoring process and also as near-term forecasts where daily or weekly variables are unavailable. This paper will first describe our process of using SAS® Text Miner to import and discover economic topics and sentiment from unstructured economic reports. The next section describes sentiment variable selection techniques that use SAS/STAT®, SAS/ETS®, and SAS® Enterprise Miner™ to generate similarity measures to economic indices. Our process then uses ARIMAX modeling in SAS® Forecast Studio to create economic index forecasts with topic sentiments. Finally, we show how the sentiment model components are used as a matrix of economic key performance indicators by topic and geography