## 5 CosMovis Network Analysis Scenario  
Our research fixed the network data based upon sentiment words MDS map and was designed to minimize users' learning and prevent distorted interpretation, by applying a metaphor based on the sentiment keyword characteristics of the nodes in the network. In order to show that the user's target can be applied widely, this section presents an ideal scenario about how our sentiment network visualization is used as a movie recommendation and how it affects a general public's decision-making process [8]. The scenario is shown as follows.  
**Scenario: Getting a movie recommendation from the cluster with similar emotions based on previous movies that the general public has watched previously.**  
This scenario describes a situation of obtaining a movie recommendation from clusters that share similar emotions based on sentiment words implied by users' previous movie preferences. Assume that the user watched \"Star Trek\" and was satisfied with the emotions provided by the movie. The user will then search for a new movie from the network. He/she will first find nodes of \"Star Trek\" in the network and then look for movies closely located to \"Star Trek.\" As a result, the user will be able to find three other movie nodes based on \"Star Trek\" as shown in red in Figure 9.  
Next, the user will focus on the heatmap to understand what kinds of emotions those three movies contain. Figure 10 on the right shows heatmaps of movie nodes A, B, and C as well as their sentiment word distribution table. Node A is for the movie \"Avatar\" and Node B is for the movie \"Ironman 3.\" One can see that heatmaps of these two nodes are very similar to \"Star Trek.\" Thus, if the user wants to have a similar emotional experience, one could watch Avatar or Ironman 3. Node C is for the movie \"Masquerade.\" According to its heatmap, this movie also contains \"touching\" aspects in addition to other emotions present in \"Star Trek.\" Therefore, if one wants a movie more emotional than \"Star Trek,\" the user could pick this movie.  
Figure 8: Comprehensive “CosMovis” constellation map of sentiment word-based movies. (Demo Site URL: [[https://hyunwoo.io/cosmovis/](https://hyunwoo.io/cosmovis/)]([https://hyunwoo.io/cosmovis/](https://hyunwoo.io/cosmovis/))). Reproduced with permission from [7]; published by [IEEE], 2014.  
We can also note that node C is for a historical genre movie, \"Masquerade,\" unlike \"Star Trek,\" \"Avatar,\" or \"Ironman 3\". This implies that sentiment-based movie similarity network can also recommend movies in different genres as long as they contain similar sentiments. Likewise, users can make more effective decisions if they want to obtain movie recommendations based on movies they have watched by understanding the network structure, selecting candidate movies among ones similar to his/her previously enjoyed movies, and analyzing sentiment word frequency of each candidate node.  
Figure 9: (**upper**) Movie “Star Trek” Poster and TF-IDF size frequency of sentiment words. (**middle**) “Star Trek” heatmap visualization. (**lower**) After selecting the movie “Star Trek (**blue node**),” three movies A, B, and C (**red nodes**) that are located closely within the same cluster are discovered. Reproduced with permission from [8]; published by [Journal of The Korea Society of Computer and Information], 2016.