Icon

NetworkAnalytics_​meets_​TextProcessing

Network Analytics meets Text Processing

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 scatter plot that visualizes the social status as well as the general mood of the users for the Slashdot data set.



Data Loading
Network creation and analysis
Text processing and sentiment analysis
Visualization of combined results from text and network mining

Supplementary Workflow

Document Creation
preprocessing
Subjectivity Corpus
tag negative words
Dictionary Tagger
link post user with referenced post/thread user
Object Inserter
extract the largest subnet from the network
Extract largest component
accumulation of positive and negative word frequencies, user scoring and binning
User Scoring
authors on a scatter plot auth score vs. hub score colored by sentiment
Scores & Sentiment on Scatter Plot
Most Positive / Negative Author
Filter article and comments of anonymous users
Preprocessing
compute authority & hub score
Network Analyzer
Slashdot
Table Reader
Tag Clouds of most positive and most negative author
Tag Cloud
edge table weighted by count of posts
Create edge table
Subjectivity corpus
File Reader
Tag Cloud of selected author
Tag Cloud of Selected Author
Joiner
tag positive words
Dictionary Tagger

Nodes

Extensions

Links