Association Rule Mining With Tweets: Thinking Outside the Basket

Ami Gates, Georgetown University

With the increasing and continued interest is text mining, and the potential for relationships between words or items, association rule mining has become a more popular technique. The classic example for association rule mining is to investigate “baskets” of items originating from transactions. The most notable such example is the “market basket”, where foods appear within transactions with greater or lower joint probabilities. However, collections of items, or baskets, are not the only application for association rule mining. Applying association rule mining to Twitter data (Tweet Text) using R offers interesting insight into words that are highly associated or correlated in a given set of Tweets. By thinking of each Tweet as a transaction, one can collect Tweets, reformat them into basket-style .csv data, and use R to apply association rule mining to discover relationships.