select * from mldb.H2OPredict(
on (select top 5 * from airlines)
on (select model_id, model, h2o_lic.license from h2o_models where model_id = 'h2odai_airlines_nlp') DIMENSION
using
Accumulate('text')
ModelType('DAI')
UseCache('false')
) as td;
*** Query completed. 5 rows found. 3 columns returned.
*** Total elapsed time was 4 minutes and 2 seconds.
text prediction json_report
----------------------------------- -------------- ----------------------------------------------
.@un I wouldn't describe chewgum sprinkled in your bathroom as an inconvenience. To help, you might find my baggage you lost & return it. {"airline_sentiment.neutral":"0.20388491","airline_sentiment.negative":"0.68529856","airline_sentiment.positive":"0.11081651"}
.@un I appreciate you looking. Can you compensate me on anything for my troubles? Still haven't taken off for a 10:30 am flight {"airline_sentiment.neutral":"0.20388491","airline_sentiment.negative":"0.68529856","airline_sentiment.positive":"0.11081651"}
.@Airline1 I appreciate that, and not to be rude, but it doesn't solve much. You should AT LEAST cover the cost of stolen goods. {"airline_sentiment.neutral":"0.20388491","airline_sentiment.negative":"0.68529856","airline_sentiment.positive":"0.11081651"}
@Airline2 #epicfail on connections in #Chicago today, extremely disappointed w/ unaccommodating customer service, rethinking loyalty {"airline_sentiment.neutral":"0.20388491","airline_sentiment.negative":"0.68529856","airline_sentiment.positive":"0.11081651"}
...