  The open-source Global Database of Events, Language, and Tone (GDELT) is the
most comprehensive and updated Big Data source of important terms extracted
from international news articles . We focus only on GDELT's Singapore events to
better understand the data quality of its news articles, accuracy of its term
extraction, and potential for prediction. To test news completeness and
validity, we visually compared GDELT (Singapore news articles' terms from 1979
to 2013) to Wikipedia's timeline of Singaporean history. To test term
extraction accuracy, we visually compared GDELT (CAMEO codes and TABARI system
of extraction from Singapore news articles' text from April to December 2013)
to SAS Text Miner's term and topic extraction. To perform predictive analytics,
we propose a novel feature engineering method to transform row-level GDELT from
articles to a user-specified temporal resolution. For example, we apply a
decision tree using daily counts of feature values from GDELT to predict
Singapore stock market's Straits Times Index (^STI). Of practical interest from
the above results is SAS Visual Analytics' ability to highlight the various
impacts of June 2013 Southeast Asian haze and December 2013 Little India riot
on Singapore. Although Singapore is unique as a sovereign city-state, a leading
financial centre, has strong international influence, and consists of a highly
multi-cultural population, the visual and predictive analytics reported here
are highly applicable to another country's GDELT data.
