DEIM Forum 2023 5a-8-5

Craft Visualization System based on Characteristic Extraction andEmotion Analysis
Zerong SONG† and Yuanyuan WANG†† Graduate School of Sciences and Technology for Innovation, Yamaguchi University
2–16–1 Tokiwadai, Ube, Yamaguchi, 755–8611 Japan
E-mail: †{c109vgw,y.wang}@yamaguchi-u.ac.jp
Abstract Crafts express regional culture in a unique way and carry on the cultural continuity of a local region
or even a country. Globalization of the economy has led to better dissemination of crafts worldwide, and the crafts
have become bridges between diﬀerent places and the world. Nowadays, users pay more attention to artifacts foundon webpages, streaming media, etc. However, it is diﬃcult for users to immediately obtain a thorough grasp of crafts
from a region because of the large number and variety of crafts in the world. Therefore, in this paper, we develop
a system for visualizing crafts based on those practiced in Japan’s Kinki area. For visualizing crafts to represent
their characteristics and emotional attributes, we utilize traditional crafts provided by the Ministry of Economy,
Trade and Industry (METI) through characteristic extraction and sentiment analysis. Finally, we evaluated theusability of the proposed craft visualization system, and we also conﬁrmed that our proposed system could help
users understand regional crafts quickly and comprehensively.
Key words traditional crafts, characteristic extraction, emotion analysis, visualization

1 Introduction

veloped craft visualization system in our ongoing work [1].
Through characteristic extraction, emotion analysis, and vi-

According to the deﬁnition of the Ministry of Economy,

sualization, we aim to make users around the world aware ofTrade, and Industry (METI)（注 1） of Japan, traditional crafts

the traditional crafts of the region, promote the crafts, andare items made by a small number of people in a given re-facilitate regional activation as well as international cross-gion for everyday use, using traditional raw materials as thecultural exchange.

primary raw material and having the main part done by thecrafts and traditional skills. Japanese traditional crafts are

2 Related Workvery popular among users all over the world because of their

In characteristic extraction, methods such as TF-IDF,

practicality, aesthetics, and craftsmanship they convey. Ac-

LDA is often used. Kowsari et al. [2] applied LDA to topic

cording to the data released by the Ministry of Economy,

modelings and other document processing tasks, such as

Trade, and Industry (METI), 240 traditional crafts are cur-

document classiﬁcation and collaborative ﬁltering. Jha etrently designated. Since 2016, when it fell below 100 bil-al. [3] presented a new and simple approach towards text-to-lion yen, the volume of traditional craft production has beenemoticon conversion and vice-versa using NLTK and Word-

gradually decreasing.

Net.

In 2020, there were approximately

54,000 related employees, a number that is also gradually

In text emotion analysis, Ahmed et al. [4] proposed an

declining. As a result, we can conclude that the number ofattention-based LSTM model to address aspect-level emo-

traditional crafts in Japan is large and widely distributed,tion analysis task based on the emotion score retrieved fromand it is diﬃcult to preserve them because there are not

the proposed dictionary to weight down the non-emotion

enough successors. For this reason, the visualization and dis-parts among aspect-related information in a given sentence.

semination of traditional crafts are particularly important.

BERT, a language representation model of the Google AI

Therefore, the purpose of this paper is to develop a craft

Language API, aims to quantify where linguistic informa-

visualization system based on the traditional crafts of thetion is captured in the network. In 2019, qualitative analysis

Kinki region in Japan. We provide the details of our de-experiments conducted by Tenney et al. [5] showed that the
BERT model can dynamically adapt to the traditional NLP
pipeline, modifying low-level decisions based on ambiguous（注 1）：https://www.meti.go.jp

information from higher-level representations.
In visual analysis,methods include wordcloud, treemap,
themeriver, etc. Chen et al. [6] proposed Tagnet, a model

learn about the basic characteristics of this traditional craft:a traditional fan made of bamboo, which was producedby painting and rib during the Edo period.

based on node linkage graph and wordcloud, which colorcodes nodes and linkages to show the emotion attributes ofTable 1 The Classiﬁcation Results of Six Dimensions for Three
Traditional Crafts.

tags. Brooks et al. [7] used an emotion-themeriver to show

Dimension

KYO Uchiwa

SHIGARAKI YakiKYO Yuzen

the changes in the number of Twitter comments contain-

Region

Kyoto

Shigaraki

Kyoto

ing diﬀerent emotion messages by representing diﬀerent emo-

Categoryfanware, yaki

textiletions through colors.

Material

bamboopottery, kiln

clothAtmospheretraditionalsabi, uniquepictorial

Manufacturingrib, painting

ﬁring

dyeing, silk

Era

Edo

Kamakura

Edo

In this paper, we use NLTK to pre-process the data and
LDA to extract feature words in the description text of traditional crafts. We use Google Natural Language API （注 2）
based on the BERT model to get the emotional scores of description texts for traditional crafts. By using Tagcloud andThemeRiver [8] to represent the feature words of traditional
crafts and their emotion attributes and the word frequencychanges of the feature words of traditional crafts in the temporal dimension.

3. 3 Emotion Analysis
In this section, we applied the Google Natural Language
API to perform the emotion analysis on the historical description texts of crafts based on the BERT model. In the
pre-processing section, we divided the historical description
text of crafts based on sentences. So we calculate the emo-

3 Characteristic Extraction and Emotion
Analysis of Traditional Crafts

tion score of each sentence and then use the average score of

3. 1 Pre-processing

craft. The range of emotion scores was -1.0 to 1.0.

all sentences to represent the emotion score of the traditional

In this section, we pre-processed the description texts. We

We deﬁned sentences with absolute values of emotion

extract the description text of “Traditional Crafts” provided

scores below 0.1 as unemotional sentences, and because un-by the Association for the Promotion of Traditional Craft

emotional sentences aﬀect the calculation of the total score,

Industries “Japan Traditional Crafts Aoyama Square,” andwe removed the emotion scores of unemotional sentences

use NLTK to divide the description texts by sentence andwhen calculating the emotion scores of crafts. Meanwhile,remove stopwords in the sentence.

the emotion attribute of traditional crafts is deﬁned as pos-

3. 2 Characteristic Extraction

itive and is shown as red when the emotion score is between

In this section, we perform characteristic extraction of

0.3 and 1.0; it is deﬁned as neutral and shown as green whencraft description texts from six dimensions. Following pre-the emotion score is between -0.3 and 0.3; and it is deﬁned

liminary studies, we examined the results of characteristic

as negative and shown as blue when the emotion score is

extraction from pre-processed description texts using NLTK,

between -1.0 and -0.3.

TF-IDF, and LDA in ﬁve dimensions. We concluded that
LDA oﬀers superior outcomes.

Figure 1 shows the color distribution of emotion attributesof traditional crafts.

After using GENSIM to vectorize words in the preprocessed description texts, we used LDA to extract the topic
words (topic: 1, #words: 20) and artiﬁcially categorize theminto ﬁve dimensions: region, category, material, atmosphere,

Figure 1 Colors and emotions.

and manufacturing.
We constructed a dictionary using Wikipedia’s Timeline ofJapanese History as a reference and extracted the temporal

4 Visualization of Traditional Crafts

dimension from the description texts.

4. 1 Visualization with Tagcloud

Using the above characteristic extraction, traditional

In this section, we generate Tagcloud utilizing the six di-

crafts are analyzed in six dimensions: region, category, ma-mensions of feature words that were derived in Section 3. 1,

terial, atmosphere, manufacturing, and era. We show theand we display the size and color of the feature words ac-classiﬁcation results of six dimensions for three traditionalcording to word frequency and emotion score. Furthermore,crafts in Table 1. Taking KYO Uchiwa as an example,

because the feature words themselves do not have emotion

the six dimensions of the classiﬁcation enable us to quickly

attributes, we calculate the emotion scores of the featurewords by averaging the emotion scores of multiple sentences（注 2）：https://cloud.google.com/natural-language

in which the feature words appear in the description texts.

browse the common words of the traditional crafts’ feature

The formula for calculating the emotion score of a featurewords to gain a general idea of the traditional crafts and canword is given in Eq. (1) as follows:∑nof Su containing Wi
i=1 Vu∑nVi =u=1 N of Su

comprehend the emotional attributes of regional traditional
crafts and the development of related feature words in dif(1)where Wi stands for the feature word, Su for the sentencein the description text that contains the feature word, N for
the number of sentences that contains the feature word, andVu for the emotion score of the sentences in the description

ferent eras. As shown in Figure 3, when the user chooses
one of the traditional crafts, such as the Tamba Tachibanaof Hyogo Prefecture, the image on the map is magniﬁed, andthe user read the Introduction and Tagcloud to ﬁnd out more
information about the traditional craft.

text that contains the feature word Wi . The ﬁnal emotion
score for the feature word Wi is represented by the variable
Vi . Additionally, the absolute value of Vu needs to be greaterthan 0.1.
4. 2 Visualization with ThemeRiver
In this section, we created a ThemeRiver chart with achronological horizontal axis and a word frequency verticalaxis using ECharts（注 3）, an open-sourced, web-based, crossplatform framework that facilitates the quick building of interactive visualization. By showing the changes in word frequency of the characteristic words in diﬀerent eras, the The-

Figure 2 The visualization interface of Hyogo Prefecture.

meRiver chart reﬂects the temporal changes in traditional
crafts in the region.
4. 3 Visualization for Traditional Crafts
In this section, we use the Kinki region as an example tovisualize traditional crafts. The interface of the visualization
system is shown in Figures 2 and 3. The system consists ofﬁve parts: map, Tagcloud, images of traditional crafts, description texts of traditional crafts, and ThemeRiver.
（ 1 ） Using Leaﬂet（注 4） and Google Maps API to present

the traditional crafts on the map.
（ 2 ） Using WordCloud of the python library to create

a tagcloud to show the six dimensions of feature words of
traditional crafts.

Figure 3 The visualization interface of Tamba Tachikui.

（ 3 ） In the part of images of traditional crafts, three col-ors of red, green, and blue are used to represent emotional

5 Evaluation

attributes in the edge of the images of traditional crafts.

5. 1 Data Collection and Processing（ 4 ） Extracting the history of the traditional crafts andWe used the publicly available data of traditional crafts

presenting the description texts.

provided by the METI, and used the description text data of（ 5 ） Using ThemeRiver to show the change in word fre-

“Traditional Crafts” provided by the Association for the Pro-quency of the feature words in diﬀerent eras.

motion of Traditional Craft Industries “Japan Traditional

The images and description texts of traditional crafts ofCrafts Aoyama Square.” From the data, we extracted 40 dif-

Hyogo Prefecture in Figures 2 and 3 are quoted from the As-

ferent types of traditional crafts in the Kinki region. Then,sociation for the Promotion of Traditional Craft Industries

through characteristic extraction, 800 feature words were

“Japan Traditional Crafts Aoyama Square”（注 5）. As shown

extracted and 339 feature words were classiﬁed manually.

in Figure 2, by entering the region, users can view all of

Through emotion analysis, we calculated the emotion scores

the traditional crafts in Hyogo Prefecture. They can alsoof traditional crafts and the emotion scores of feature words.
Finally, we developed a system for visualizing crafts.

（注 3）：https://echarts.apache.org/zh/index.html（注 4）：https://leaﬂetjs.com（注 5）：https://kougeihin.jp/

Table 2 The results of the usability questionnaire by SUS scores
Q1
SUS Score

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q10 Ave70.8 79.2 83.3 83.3 68.8 79.2 83.3 79.2 83.3 66.7 77.7

5. 2 Usability of Visualization System

the visualization system received a high rating in terms ofIn this section, we evaluated the usability of the Visualiza-

overall performance and functionality. However, it received

tion system using the System Usability Scale (SUS ) createda moderate rating in terms of demonstrating the emotional

by Bangor et al. [9]. Seven males and ﬁve females in their 20s

attributes of traditional crafts. We also requested feedback

served as subjects. Following a visualization demonstrationfrom the subjects. As a result, we will make improvementsof the system, subjects responded to the following questions.

in the future.

Q1. I think that I would like to use this system frequently.

In the future, we intend to expand the experimental

Q2. I found the system unnecessarily complex.

dataset to include data from other regions as well as crafts

Q3. I thought the system was easy to use.

from other countries to improve the applicability of the sys-

Q4. I think that I would need the support of a technicaltem and to improve the presentation of emotion attributes

person to be able to use this system.

in the craft visualization system. Furthermore, we plan to

Q5. I found the various functions in this system were well

explore the relationships between traditional crafts of Japan

integrated.

and present them in the visualization system.

Q6. I thought there was too much inconsistency in this system.
Q7. I would imagine that most people would learn to use

Acknowledgment
This work was partially supported by JSPS KAKENHI Grant
Numbers JP19H04118 and JP21K17862.

this system very quickly.
Q8. I found the system very cumbersome to use.
Q9. I felt very conﬁdent using the system.
Q10. I needed to learn a lot of things before I could get goingwith this system.
The System Usability Scale (SUS ) was rated on a 5-point
scale (1: strongly disagree, 2: disagree, 3: neutral, 4: agree,
and 5: strongly agree). We calculate the SUS score fromthe above questions. Firstly, each subject’s rating of every
positive question (Q1, Q3, Q5, Q7, Q9) minus 1 (rating-1),
and each subject’s rating of every negative question (Q2, Q4,
Q6, Q8, Q10) is subtracted from 5 (5-rating) to adjust thescore for each subject’s question to make sure it is between
0 and 4. Secondly, we took the average subject’s rating ofeach question and multiplied it by 25 to convert it to a SUS
score scale from 0 to 100.
As shown in Table 2, the average score of all question items
is 77.7. According to general guidelines on the SUS score,A) Excellent: >80.3, B) Good: >68.0-80.3, C) Okay: >68.0,
D) Poor: >51.0-68.0, E) Awful: >51.0, given that the SUS
score was “Good;” we could conﬁrm that our proposed visualization system is very useful.

6 Conclusion
In this work, we developed a craft visualization system bycharacteristic extraction and emotion analysis of traditional
crafts. Through the questionnaire, we received a “Good” rating from SUS scores, which indicates the high usability of thevisualization system. In addition, we conducted a user satisfaction survey, and based on the results, we concluded that

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