Social network analysis is one the trending topics in the world of data science. As we have seen throughout the chapter, these platforms not only provide us with ways to connect but they also present a unique opportunity to study human dynamics at a global scale. Through this chapter, we have learned some interesting techniques. We started off by understanding data mining in the social network context followed by the importance of visualizations. We focused on Twitter and understood different objects and APIs to manipulate them. We used various packages from R, such as TwitteR
and TM
, to connect, collect, and manipulate data for our analysis. We used data from Twitter to learn about frequency throughout. Finally, we presented some of the challenges posed by social networks words and associations, popular devices used by tweeple, hierarchical clustering and even touched upon topic modeling. We used ggplot2
and wordcloud
to visualize our results to the data mining process in general...
Argentina
Australia
Austria
Belgium
Brazil
Bulgaria
Canada
Chile
Colombia
Cyprus
Czechia
Denmark
Ecuador
Egypt
Estonia
Finland
France
Germany
Great Britain
Greece
Hungary
India
Indonesia
Ireland
Italy
Japan
Latvia
Lithuania
Luxembourg
Malaysia
Malta
Mexico
Netherlands
New Zealand
Norway
Philippines
Poland
Portugal
Romania
Russia
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Taiwan
Thailand
Turkey
Ukraine
United States