In this step, we calculate the sentiment of each tweet. We create the new table _tweets_sentiment_ which groups the tweets of _l3_ view on the basis of id, sums up the polarity of each word and assign each tweet a sentiment label such as positive, negative or neutral.
Create _tweets_sentiment_ table. Each row of _tweets_sentiment_ table stores the sentiment of the tweet. Run below command in the Hive query editor in Hue
create table tweets_sentiment stored as orc as select id, case when sum( polarity ) > 0 then 'positive' when sum( polarity ) < 0 then 'negative' else 'neutral' end as sentiment from l3 group by id;
Sample rows of _tweets_sentiment_ table are
What is the sentiment of tweet with id as 330043911940751360?