Abstract:
In the past few years, the usage of social media around the world has impacted
people’s lifestyles because of the widespread use of recommender systems. As Twitter
being of the main social media platforms, a high interest on text mining for this social
media platform has been observed. Because of this, the need of exploring relationships
among words in Twitter data is of a high importance. The main purpose of this thesis
is to observe existing association rules mining techniques and use those techniques to
build correlations between words in tweets. Market Basket example is the most
common example used in association rule mining. By using the logic of market basket
practice, this study aims to use a similar approach to build a logical file composed of
tweets to understand the associations between tweets. This thesis allowed me to
understand better on the steps needed to take, in order to have a well formatted file
containing the tweets by using R programming. Results show us that different words
between different tweets have a high measure of Lift, making the association rules
between words meaningful.
