Data clustering by theme

Now that we have implemented clustering, we can restrict the data by specific theme, and thus possibly observe division among themes. We will do this with both Kmeans and DBSCAN (which had a hard time giving us pertinent results here). Here we can compare the non-clustering solution with the clustering, and additionally we can look at the Principal Component Analysis (PCA) which gives valuable information as to why clusters look the way they do.

Economy Education, culture, media Foreign policy Infrastructure, planning, environment Political regime Public finances Security policy Social policies kmeans DBSCAN

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Analysis & Discussion

The first thing we can notice is that some issues are more subjects of debate than others. The main suspects here are social policies, foreign policies and infrastucture/planning/environment, which us Swiss people can certify as being questions that divide people in our country. French-speaking Switzerland is known for being more open to questions like social assistance, the European Union and the environment. These clear cuts were expected and they reinforce the idea of a Röstigraben in this country in the last decades. We can however get a glimpse of a city/countryside divide, although it is quite overshadowed by the language divide when looking at older data.