## 4 Results
### Misalignment Visualization  
Table 7 offers some insights into the amount of misalignment in model topic assignments with the SME-assigned categories. The table shows that topics (T) assigned by the model align with only 6 of the 11 SME-assigned categories. Most topics assigned by the model related to the SME category of driving safety. The representative keywords from the top 20 list, and as visualized in the word cloud (Figure 6), offer a glimpse into the reason for the topic assignment. For instance, the model assigned topic 1 to documents that contained _sign_ as the dominant keyword. Those documents were mostly within the SME-assigned category of driving safety. However, as shown in the table, the model assigned different topics to documents that also pertained to the SME classification of driving safety. The table also lists the number of unique SME categories (USC) contained within each of the model-defined topic categories. Topic 3 achieved the best alignment with the SME category of cybersecurity with a single unique assignment. Topic 9 achieved the worst alignment because it contained all 11 SME categories.  
The topic modeling output is a matrix of documents (rows) and topic probabilities (columns) that represent the mix of all topics in a document. However, the topic assigned to a document is the one with the highest probability. Similar probability vectors of topic assignments will cluster in feature space. However, it is not possible nor practical to visualize clusters of documents, each with 11 topic probabilities or features. Fortunately, uniform manifold approximation and projection (UMAP) is a technique used to visualize high-dimensional data in two-dimensional space by embedding features in a manner that preserves their original structure and relationships [40].  
Figure 7: Topic model hyperparameter optimization.  
Figure 8 shows the UMAP of topic model probabilities, labeled by the topic model assignments. It is evident that the document clusters reflect the maximum probability topic assigned. The chart also reveals how documents contain a mix of topics because they form overlapping clusters, such as near the coordinates (7.5, 5.0). As highlighted by the dotted line boundaries shown, clusters such as topic 2, topic 5, and topic 8 are more isolated and homogeneous in topic mix. Figure 9 labels the UMAP of these topic model probabilities with the SME topic assignments to highlight the misalignment between model-assigned and SME-assigned categories. It is clear from the chart that even the remote clusters are no longer homogeneous.  
\begin{table}
\begin{tabular}{c c c c} \hline
**T** & **Majority SME Category** & **USC** & **Topic Model Keywords Relevant to SME Category** \\ \hline
0 & Driving Safety & 4 & insurance, accident, risk \\
1 & Driving Safety & 5 & sign \\
2 & Cybersecurity & 4 & authenticate, register \\
3 & Cybersecurity & 1 & certificate, privacy \\
4 & Driving Safety & 5 & green, camera, occupy, interact \\
5 & Multi-Vehicle Networking & 4 & micro, anomalial, slice \\
6 & Platooning & 7 & platoon, host, gateway, command, trail, brake, automate \\
7 & Smart Parking & 4 & charge, battery, transfer, promotion \\
8 & Driving Safety & 3 & video camera, capture, situation, stop \\
9 & Driving Safety & 11 & interfere, collision, crash, stop, navigation, warn \\
10 & Wireless Communications & 6 & telecommunication, cellular, transceiver, packet, switch, cell \\ \hline \end{tabular}
\end{table}
Table 7: Topic model confusion with SME categories.  
Figure 8: UMAP of topic model probabilities, colored by model topic assignments.