## 1 Introduction  
### Background and Purpose of Research  
At present, we are faced with an enormous amount of information every day due to the vast growth of information and communications technology. Thus, there is increased interest in effective data processing and analysis. In particular, big data is playing an increasingly important role since it is suitable for refined and semantic processing, even if the amount of data is considerable or if its structure is complex [1]. Big data has also attracted great attention in the field of data visualization primarily for the design of efficient processing and semantic analysis. Data visualization is a redesigned concept of data analysis with better readability, offering distinct insights that cannot be grasped from a table or graph [2]. Network visualization is a visual tool to semantically analyze data if there is a massive amount or if the structure is complex [3]. Therefore, this study aims to demonstrate massive collective intelligence data through network visualization. This study also proposes an intuitive and semantic analysis method by designing multilevel sentiment network visualization based upon emotion words [4]. Social network analysis plays a significant role in understanding and finding solutions for society-functional problems by examining the original structure and relationships of the network. Therefore, network visualization is applied in a wide variety of fields, including network analysis based on data similarity, network analysis about social-scientific situations, graph theory, and recommendation systems.  
Force-directed graph drawing algorithm is a standard layout algorithm for designing a network graph. It is considered to be highly useful since it allows related nodes to form a cluster [5]. However, the location of the node varies each time the graph is formed if a force-directed graph drawing algorithm is used because the entry value of the node's location is random and the eventual position is determined by relative connections between nodes. Therefore, users must repeat the learning of the system if in a force-directed network, since the absolute location information is not fixed (Figure 1). This is a notable drawback. Such difficulties can become great obstacles when interpreting the network if it consists of a considerable amount of data. Furthermore, the collective intelligence can deliver the wrong data if it is visualized based on a force-directed layout since locations of nodes may vary.  
Following a preliminary study of ours [6; 7; 8] which introduced the sentiment network visualization approaches that are the basis of this work, we designed a multilevel sentiment network visualization so as to facilitate intuitive understanding of the complex collective intelligence data. Also, we present an approach to find solutions for those difficulties of a force-directed layout. Specifically, the primary contributions of our work are:  
* More complete description of the sentiment movie network developing process: we present the explanation of flock algorithm method. Even if nodes increase in large scale, with this method, nodes won't be overlapped making bigger sentiment network configuration possible with improved delivery.
* Considering more diversity visual metaphorical graphic model, and conducting a survey about selection of graphic to better support interaction with the sentiment movie network.
* An evaluation for comparing the awareness level on network location.
* An evaluation for comparing between its final visualization map (Network + MDS+ Constellation Metaphor) and our previous visualization map [8].  
Figure 1: Force-directed layout network (**left:** 300 nodes; **right:** 400 nodes). Locations of nodes continue to change whenever data is added or modified. Reproduced with permission from [6]; published by [IEEE], 2015.