I have written the following post about Social Network Analysis and Topic Modeling of codecentric’s Twitter friends and followers for codecentric’s blog:
Recently, Matthias Radtke has written a very nice blog post on Topic Modeling of the codecentric Blog Articles, where he is giving a comprehensive introduction to Topic Modeling. In this article I am showing a real-world example of how we can use Data Science to gain insights from text data and social network analysis.
I am using publicly available Twitter data to characterize codecentric’s friends and followers for identifying the most “influential” followers and using text analysis tools like sentiment analysis to characterize their interests from their user descriptions, performing Social Network Analysis on friends, followers and a subset of second degree connections to identify key players who will be able to pass on information to a wide reach of other users and combing this network analysis with topic modeling to identify meta-groups with similar interests.
Knowing the interests and social network positions of our followers allows us to identify key users who are likely to retweet posts that fall within their range of interests and who will reach a wide audience.
The entire analysis has been done in R 3.4.0 and you can find my code on Github.