## 4 Visualization Proposal
### Sentiment Movie Network  
In this session, we aim to describe the basic structures of suggested graphs and examples. Locations of nodes can be altered depending on the main sentiment word from the movie review. The suggested graph is similar to the artifact actor network, a type of multi-layered social network. the artifact actor network connects between the artifact network and social network using the semantic connection. Thus, it expresses the semantic relation between two networks [34]. In our proposed graph, we connected sentiment words on the 2-dimensional scaling map with movie network. In this paper, we  
Figure 4: (**a**) Heatmap of “Don’t Cry Mommy” showing single emotions (Furious, Outraged). (**b**) Heatmap of “Snowpiercer” that shows various emotions (Cruel, Pitiful, Lonely, Bored, Funny, Great, and Energetic). Reproduced with permission from [6]; published by [IEEE], 2015.  
referred to this network as the sentiment-movie network. Figure 5 shows the basic structure of the sentiment movie network [6]. As shown in Figure 5, the suggested graph is comprised of two layers. The first layer is called the semantic layer. It consists of semantic points based on the 36 sentiment words. The semantic point of the sentiment word is located at an initially set value. It is immovable. The second layer is called the network layer, which includes nodes comprising the movie network. Each movie node forms the edge of other movie nodes based on similarities. It also forms imaginary edges with the sentiment word in the two-dimensional distribution map based on the sentiment word connoted by the pertinent node. Nodes connected by edges have both attractive force and repulsive forces based on a force-directed algorithm. By contrast, semantic points of sentiment words are immovable, leaving only attractive force. For edge composition between nodes, we calculated cosine similarity between movies based on TF-IDF scores of the 36 sentiment words. The similarity between movie A and movie \(B\) or \(SIM\) (\(A\), \(B\)) is shown as follows. \[SIM(A,B)=\frac{\frac{n}{i=0}A_{i}*B_{i}}{\sqrt{\frac{n}{i=0}\left(A_{i}\right)^ {2}}*\sqrt{\frac{n}{i=0}\left(B_{i}\right)^{2}}} \tag{4}\]  
The edge between each node and the semantic point sets up a fixed threshold value and generates an edge by designating a sentiment word with a value that is greater than a threshold value as semantic feature. Figure 6a,b show an example that the location of a node on the graph can be altered depending on the frequency of sentiment word indicated in the Heatmap Visualization [7; 8]. Figure 6a shows that the node is located in the space of the sentiment word with overwhelmingly high frequency. If a movie has more than two frequent sentiment words, its nodes will be located in the way, as shown in Figure 6b. If shown in the heatmap visualization of Figure 6b, nodes in \"Snowpiercer\" are influenced by keywords such as Pitiful, Bored, Energetic, Great, and Cruel. In addition, nodes are particularly influenced by locations of \"pitiful\" and \"bored\" since those two are the strongest that they affect nodes to be placed around the negative y-axis. As such, frequent sentiment words will place nodes. To show users a host of nodes in a meaningful position without overlapping, flock algorithm has been applied. Flock algorithm consists of separation, alignment, and cohesion [35]. We composed nodes applying separation method because nodes of developed visualization do not move and have no direction. **Solution**: One node compares all nodes to each other's positions and moves in opposite directions if overlaid. Figure 5: The multilevel structure of the sentiment movie network. Reproduced with permission from [6]; published by [IEEE], 2015. ```
Input:\(nodes\)\(\rightarrow\)Node objects to be displayed on the screen. Output: Node objects whose locations have been recalculated so that they are distributed without overlap.