This workflow combines two pre-processing techniques: network analytics and text processing.The goal of the text processing part is to identify the general mood of a user e.g. positive, negative or neutral based on the sentiment of its posts and comments. The goal of the network analytics part is to compute the social status e.g. leader or follower of a user in the forum community. The visualization part condenses the two result sets into a single scatterplot that visualizes the social status as well as the general mood of the users for the slashdot data set.
This is a mini-workflow for Data WareHousing, in particular it archiviates daily tweets.History is saved in a PostGreSQL database (but it could be any database). This workflow is supposed to run every day. Every day it collects yesterday's tweets from Twitter and last month's tweets from a PostGreSQL database and blends them together to produce the tag cloud of the month. ... and yes! They blend.
BLOG: Twitter meets PostGreSQL https://www.knime.org/blog/Twitter-meets-PostGreSQL