As you can see from the results above, we did not get the initial groupings that we had expected. The clusters that were easier to interpret were clusters that had a huge probability of discussing a topic that was appropriate to the members of the clusters. For example, the Family Cluster is made up primarily of Family members, and the most common topic is Family. The same goes for the Ateneo Cluster , which is made up of students and co-teachers, and the number one topic is Ateneo. We also have the Friend Cluster 1, which are all made up of friends from various points in time, but the topics are more or less similar -- capturing Life Discussions where most of our talks would be catching up and updating each other on life.





Then, we have clusters have clear topics, but mixed members. The Data Cluster captured all three data friends, but is made up of more than that. Looking at the specific persons behind the titles, these are friends that have, at one point or another, expressed interest in the topic as well. Interestingly, the co-teachers and students included in this clusters were the people I went with to a Data Science boot camp in 2017. For the Hong Kong Cluster , these are basically similar to Friend Cluster 1, with Life Discussions being a big part topic, but mixed in with Hong Kong specific conversations.





Lastly, we have hard to interpret clusters. Cluster 2, for example, is made of a mixed members, with the majority of topics being Ateneo and Life Discussion, suggesting an intersection of Ateneo connections and friends, in other words co-teachers or students who have become similar to friends. While this may be possible for the students and co-teachers in this group, we also have work friends and college friends in the same group, which, by looking at our conversations, are non-Ateneo friends who I talk to about teaching in Ateneo. Finally, Cluster 6 and 7 are outliers , where Cluster 6 includes my girlfriend, who I talk to everyday and makes up more than half of all messages, which makes sense why Daily Life is a popular topic. Cluster 7 contains a one friend, made up completely of the undefined topic, which makes this specific cluster to interpret even after looking at the conversations.







