A case study on travel related tweets.
The following tasks were carries out:
The language was detected for each tweet.
The most common n-grams were visualized.
Clustering was used to extract clusters/topics from users' tweets.
An API was built to infer the right cluster/topic for each new input tweet.
Business insights were derived from created clusters.
A supervised learning task was created in order to predict the number of tweet favourite.