## 4 Visualization Proposal
### Heatmap Visualization  
Heatmap visualization is a method consisting of rectangular tiling shaded in scaled color [29; 30]. It is used to search for anomalous patterns of data metrics as outlined in previous research [31]. To comprehend convergences, the method described by [PERSON] et al. [32] was used. A recent study proposed 100 cases of IT fractures, visualized and analyzed using the 2D parametric fracture probability heatmap [33]. The proposed map projection technique can project the high dimensional proximal femur fracture information onto a single 2D plane. Also, they applied heatmap in order to present the level of risk. In this research, we showed TF-IDF size frequency of sentiment words in a heatmap, utilizing coordinate space in 2-dimensional distribution map of each sentiment word in order to visualize a sentimental distribution graphic for each movie node. The detailed research process is shown below. First, we measured space among the selected 36 sentiment words and analyzed correlations in order to design a two-dimensional sentiment word distribution map. We then conducted multi-dimensional scaling (MDS). We conducted a survey on the semantic distance among 36 sentiment words by enrolling 20 college students majoring in Digital Media Technology. These 36 sentiment words were placed on both axes (\(36\times 36\)) and the distance between words was scored in a range of plus/minus 3 points by considering their emotional distance. We used UNICET to facilitate a variety of network analysis methods based on data obtained from the 20 survey participants [10]. We also created Metric MDS as shown in Figure 3 based on the semantic distance among movie review semantic words. As a result, positive emotions such as \"Happy\" and \"Surprise\" were distributed on the right side of the X-axis while negative feelings such as \"Anger\" and \"Disgust\" were distributed on the left side. Active emotions that were generally exaggerated gestures such as \"Fear\" and \"Surprise\" were distributed on the top of the Y-axis while static emotions such as \"Sad\" and \"Boring\" were on the bottom. Furthermore, each type of sentiment words was clustered concretely based on particular characteristics such as \"Happy,\" \"Surprise,\" \"Boring,\" \"Sad,\" \"Anger,\" \"Disgust,\" and \"Fear\" on the two-dimensional distribution map. Results showed that cluster characteristic \"Surprise\" could be divided into \"Happy\" and \"Fear\" clusters. This implies that both overflowing joy and sudden fright are dominant in particular movies [10]. \begin{table}
\begin{tabular}{c c} \hline \hline
**Clustering Characteristics** & **Sentiment Words** \\ \hline Happy & Happy, Sweet, Funny, Excited, Pleasant, Fantastic, Gratified, Enjoyable, Energetic \\ Surprise & Surprised, Ecstatic, Awesome, Wonderful, Great, Touched, Impressed \\ Boring & Calm, Drowsy, Bored \\ Sad & Pitiful, Lonely, Mourful, Sad, Heartbroken, Unfortunate \\ Anger & Outraged, Furious \\ Disgust & Ominuous, Cruel, Disgusted \\ Fear & Scared, Chilly, Hornified, Terrified, Creepy, Fearsome \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Final sentiment words. Reproduced with permission from [6]; published by [IEEE], 2015. Heatmap visualization was then designed based on a two-dimensional sentiment word distribution map as well as frequencies of these 36 sentiment words, which consisted of a two-dimensional distribution map. One optional movie was needed to design the heatmap. We measured frequencies of sentiment words on each movie by contrasting sentiment words in the movie review data obtained through the data construction process and sentiment words in the morphological dictionary. In addition, we measured numerical values by calculating the TF-IDF score to lower the weight of particular sentiment words that frequently emerged regardless of the typical characteristics of the movie. Therefore, TF-IDF score on each sentiment word could be interpreted as a numerical value reflected on the heat map visualization graph for target movies.