Interpretation of PCA visualisation
Do not apply PCA to categorical data

PCA attempts to find the dimensions which contains the most variance in a dataset. When you have categorical variables, the distance between points and the variance captured by a variable are ill-defined. First of all, you have no proper distance measure to tell how far apart two categories are, and secondly, the ordering of your categories will have a big impact. Ordering months categorically will result in a small distance between January and February, but a large one between December and January. If you're categorizing things like broadcaster or channel, there is no natural order to put things in, so your distance measure will be essentially meaningless. Your PCA plot looks strange because you have applied it to categorical data - this is not a meaningful representation of your data.