## 2 Materials and Methods
### Network Visualization and Layouts  
A number of studies have been conducted on network visualization methods, including several recent studies on user's perception. For example, [PERSON] et al. [20] have introduced a technique called motif simplification, in which common patterns of nodes and links are replaced with compact and meaningful glyphs, allowing users to analyze network visualization easily.  
While this method identifies the maximal motif more accurately, even enabling the estimation of size through glyph and interaction, it has several difficulties for ordinary users. For example, usersmust put considerable effort toward learning concepts of motif and interpreting glyph. In addition, they have difficulty in discovering the optimal set of motifs.  
Another study by [PERSON] et al. [21] presented a tool called \"Knot\" with a focus on the analysis of multi-dimensional and heterogeneous data to achieve interface design and information visualization in a multidisciplinary research context.  
Furthermore, [PERSON] et al. [22] suggested methods to solve clustering ambiguity and increase readability in network visualization. That paper states that major challenges facing social network visualization and analysis include the lack of readability of resulting large graphs and the ambiguous assignment of actors shared among multiple communities to a single community.  
They proposed using actor duplication in social networks in order to assign actors to multiple communities without substantially affecting the readability. Duplications significantly improve community-related tasks but can interfere with other graph readability tasks.  
Their research provided meaningful insights as to how central actors could bridge the community. However, it also left confusions when distinguishing duplicated nodes and analyzing visualizations that exceeded a certain size since node duplications could artificially distort visualization.  
[PERSON] et al. [23] present a novel method that uses semantic network analysis as an efficient way to analyze vaccine sentiment. This study enhanced understanding of the scope and variability of attitudes and beliefs toward vaccination by using the Gephi network layout.  
These four studies mentioned above all aimed to improve network visualization from the perspective of users, with a particular focus on settling challenges of visualization distortion and existing network through users' learning of new technologies. However, network layouts used in previous studies could not handle overlapping of nodes when faced with increasing amounts of data.  
In addition, the user may have to repeat the system since the location may vary. Furthermore, these network layouts were inefficient in that users had to check each node one by one in order to identify characteristics of a cluster.  
Our research is consistent with previous studies in that it will fix the network data based upon sentiment words. It was designed to minimize users' learning and prevent distorted interpretation by applying a metaphor based on the characteristics of nodes in the network.  
While previous studies made recommendations based on user's individual information, we further managed user's experience data as in \"emotional review data felt during the movie watching,\" thus enriching emotional attributes that fit the purpose of movie recommendation.