  Economic Policy Uncertainty (EPU) is a critical indicator in economic
studies, while it can be used to forecast a recession. Under higher levels of
uncertainty, firms' owners cut their investment, which leads to a longer
post-recession recovery. EPU index is computed by counting news articles
containing pre-defined keywords related to policy-making and economy and convey
uncertainty. Unfortunately, this method is sensitive to the original keyword
set, its richness, and the news coverage. Thus, reproducing its results for
different countries is challenging. In this paper, we propose an unsupervised
text mining method that uses word-embedding representation space to select
relevant keywords. This method is not strictly sensitive to the semantic
similarity threshold applied to the word embedding vectors and does not require
a pre-defined dictionary. Our experiments using a massive repository of Persian
news show that the EPU series computed by the proposed method precisely follows
major events affecting Iran's economy and is compatible with the World
Uncertainty Index (WUI) of Iran.
