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.
10 Comments
What is the Girhub repo url for the code used
why is she shouting?
Amazing, thank you for addressing the theory and the coding in great clarity.
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association rules comprise the data mining task of relating data to their patterns
Hair flip at 1:07 is sexy af
amazing content!! awesome explanation and she's a great presenter with nice sense of humor.
This was interesting because it confirmed to me that association rule mining can be used for tweets. Knowing that will help with a research idea that I want to do a possible PhD on.
I have never seen a more thorough or engaging explanation of a data science concept, coupled with such an interesting use case. Massive Kudos Dr. Gates. And thank you for sharing this knowledge.
Dr. Gates is my go to person for any statistics study.