Figure 4
From: Detecting political biases of named entities and hashtags on Twitter
Visualization of the semantic components of our (a) Complete PEM and (b) Polarized PEM embeddings. We project these components onto a plane by calculating t-SNE values. Both results are reasonable, but the Polarized PEM results tend to encourage semantically-related words to be closer to each other. For example, #familiesbelongtogether and #keepfamiliestogether are used similarly in practice and they are close to each other in the embedding from our Polarized PEM model