Optimization of Motorcycle Riders Categorization Based on Emotion
Using Decision Tree Analysis
Kodai Obata*

Masashi Sugimoto†

Noriko Nagata‡

Kwansei Gakuin UniversityA BSTRACT
In the present research, we optimized emotional rider categorization
using decision tree analysis. First, we asked participants to evaluate four emotions ( enjoyable/brisk,pleasant/comfortable,boring/unsatisfying, and uneasy/scary ) toward 90 motorcycle pictures. Then, we reduced the amount of evaluation times(from two to seven evaluation). The optimized model succeeded incategorization with 85% accuracy. The optimized model also succeeded in replicating the emotion pattern of the clusters in the original model. The results indicate the high validity of the optimizedmodel.

rider of it, the road it runs on, and the environment it runs in.

Index Terms: Human-centered computing—Human computer interaction (HCI)—HCI design and evaluation methods—User models; Human-centered computing—Human computer interaction(HCI)—HCI design and evaluation methods—User studies

2.3 Procedure
First, participants provided their demographic information (e.g.,age, sex and motorcycles they possess). Then they were instructed
to imagine they were on the motorcycle in the picture and rate
how much they feel each of the four emotions; enjoyable/brisk,
pleasant/comfortable,boring/unsatisfying,uneasy/scary.
They used five-point Likert evaluations (1: It does not evoke thefeeling at all to 5: It evokes the feeling a lot. ). They completed
360 emotion evaluations in total.

1 I NTRODUCTION
These days, the online advertisement is individuated. The individuated advertisement promotes purchasing based on user’s web accesshistory and purchase history. Another way to individuate users isbased on sensitivity tendencies. For example, studies that categorized users by preference characteristics and sensitivity tendencies
have been attracting attention [2, 3].
Sugimoto [3] conducted experiments to categorize motorcycle
riders. Participants were instructed to imagine being on the motorcycle in the picture and rate how much they feel each of thefour emotions; enjoyable/brisk,
pleasant/comfortable,boring/unsatisfying,uneasy/scary . As a result, the participants
were categorized into seven rider types. However, the visualizationof sensitivity tendencies value required for the user categorization
had a problem with involving a lot of time and effort [4]．
The present paper aims to construct an optimized rider categorization model that requires much less cost in time, and effort.
2 M ETHOD
2.1 Participants
The 2,425 participants for this study (2374 males and 51 females,
M = 45.28, SD = 10.52) completed an Internet-based survey. All
participants are Japanese, and held licensure for regular or heavy
motorcycle operations. Each had at least one motorcycle (any displacement).
2.2 Materials
2.2.1 Motorcycle pictures
We used the same motorcycle pictures as the previous study [3].
The pictures contained whole parts of a moving motorcycle and the* e-mail: kodai-O@kwansei.ac.jp† e-mail:sugimoto.masashi@kwansei.ac.jp‡ e-mail:nagata@kwansei.ac.jp

2.2.2

Categorization data of riders through emotions
evoked in motorcycle riding
We used data from the previous study [3]. The data are from 240
participants and contain four emotional evaluations of the 90 motorcycle pictures. The data also contain the clusters in which theparticipants are categorized: standard riders (cluster 1),
positive riders (cluster 2),
cool riders (cluster 3),
super-positiveriders (cluster 4),
own-paced riders (cluster 5),
active riders(cluster 6), and aggressive riders (cluster 7).

3 R ESULT
3.1 Construction of the optimized model
3.1.1 Analysis target data
In the analysis, we used the data from the previous study (previousdataset) [3], and those we gathered from 2,425 participants (presentdataset). Both datasets were composed of evaluation data of fourkinds of emotion toward 90 motorcycle pictures. The dataset ofthe previous study also includes clusters of each participant, but thepresent dataset does not.
3.1.2 Result of analysis
In the dataset of the previous study, we conducted decision tree
analysis whose target variable is seven clusters and whose predictor variable is 360 emotion evaluation data. In the analysis, we usedstatistic software R and its rpart package. Considering evaluation times and evaluation accuracy, we adopted a categorizationmodel whose minimum data size in the leaf is three (Table 1). Table 3 shows the evaluation accuracy of the models. In the model,
we regarded the category of each leaf as the cluster that contains
the most number of participants. Using this analysis, we extracted
if-then rules based on the evaluation scores to the picture. Then, weadopted these if-then rules to the present dataset and categorizedparticipants into seven clusters: standard riders (cluster 1’),positive riders (cluster 2’), cool riders (cluster 3’),super-positive riders(cluster 4’),own-paced riders (cluster 5’), active riders (cluster 6’),and aggressive riders (cluster 7’). This rule allows us to categorize all participants at least two-times evaluation and at most seventimes evaluation.In addition, we represent the performance of thedecision tree breakdown using sanky diagram, as well as the previous study which used sanky diagram to represent multi-generationtransmission of individuals [1]. Every time participants evaluatedthe picture, they were divided into two subgroups (Fig. 1), and theproportion of the specific type increased (Fig. 2).

Figure 1: A decision tree in which the minimum data number of each leaf is 3.

Table 3: Euclidean distance between the scores of the clusters in thepresent model and those in the previous model.

Table 1: Categorization accuracy for minimum number of data in aleaf and its evaluation times.

the Euclidean distances between clusters derived from the originalmodel and clusters from the optimized model, based on their average emotional evaluation scores. They are the closest in standard
riders (cluster 1 and 1’), positive riders (cluster 2 and 2’), ownpaced riders (cluster 5 and 5’), and aggressive riders (cluster 7 and7’). The distances were the second closest in cool riders (cluster3 and 3’), super-positive riders (cluster 4 and 4’), and active riders (cluster 6 and 6’). Although there were minor discrepancies inthese clusters, they are acceptable because the clusters were adjacent pairs in the previous data.
In the previous model [3], participants needed 360 evaluation
times to be categorized. However, the present model could categorize them with 2 to 7 evaluation times with 85% accuracy. Thissuggests the high efficiency of the present model.

3.2 Confirmation of the validation
3.2.1 Analysis target data
We targeted the dataset of the present research.

5 C ONCLUSION
In the present research, we constructed a rider categorization modelusing decision tree analysis. We succeeded in reducing 98-99.4% ofevaluation times and constructed a model requiring much less costin time, and effort with 85% accuracy. In addition, the clusters in
the optimized model showed the same emotion patterns with thosein the original model. This indicates the high validity of our model.

Figure 2: The number of the participants in each node, and its breakdown. The broad path indicates larger number of the participants,
and the thicker blue indicates the higher proportion of a specific typeat the node.

3.2.2 Results of Analysis
We categorized the dataset based on the if-then rules we made. Table 2 shows the average emotion evaluation scores in each clusterof the previous and the present study. Table 3 shows Euclidean
distance between the scores of the present study (1’-7’) and thoseof the previous study (1-7).
Table 2: Average emotional evaluation scores of each cluster.

4 D ISCUSSION
We constructed the optimized rider categorization model, which reduced 98-99.4% original evaluation times with 85% accuracy using
decision tree analysis. This optimized model succeeded in categorizing participants into clusters whose emotion evaluation patterns are are similar with that of the original model. We calculated

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