Overview/Description
Text mining facilitates social network analysis, giving analysts the ability to capture people's sentiments about various topics. Explore how text mining and social network analysis can greatly impact many diverse areas.

Target Audience
All individuals who are new to predictive analytics and wish to use it to optimize their business performance; business leaders; analysts; marketing, sales, software, and IT professionals who want to add predictive analytics to their skill set; and decision makers of any kind

Prerequisites
None

Expected Duration (hours)
0.9

Lesson Objectives

Text Mining & Social Network Analysis

start the course

identify key characteristic of text mining

assign within document predictor variables for text mining

recognize key methods for text normalization

assign across document predictor variables for text mining

recognize the use of term frequency and inverse document frequency measures for text mining

identify key characteristics of sentiment analysis

identify examples of text mining applications

identify key characteristics of social network analysis

distinguish between ego-centric and network-centric analysis

identify key features of social network mapping

recognize key terms and concepts used in social network and media analysis

Course Number:
df_prma_a13_it_enus

Expertise Level
Intermediate