Extracting Kansei Evaluation Index
Using Time Series Text Data: ExaminingUniversality and Temporality
Runa Yamada, Sho Hashimoto(B) , and Noriko Nagata(B)Kwansei Gakuin University, 2-1, Gakuen, Sanda, Hyogo, Japannagata@kwansei.ac.jp

Abstract. In recent years, aﬀective needs, such as usability and comfort, have attracted attention, along with conventional manufacturingneeds, such as function, price and reliability. Therefore, when designing
products, it is necessary to accurately and eﬃciently reﬂect user’s aﬀective needs on product design. To that end, we clarify the user’s emotions and impressions, in terms of the ways that people feel about products. However, they are assumed to be constant regardless of time-series,
impressions that are inﬂuenced by time-series and impressions that areused universally within a certain period are mixed. As the user’s emotions
and impressions depend on time-series, it is necessary to deal with themseparately, by time-series. In this study, based on time-series changes in
the appearance frequency of evaluation words, we classiﬁed the Kansei
evaluation index, according to whether it changes by time-series or not.
In the proposed method, for each evaluation word, a state-space model
is ﬁrst used to extract the information of seasonal and trend variations.
Second, by clustering this information, the evaluation terms are separated into several clusters. This method was applied to the fashion ﬁeld,
where time-series eﬀects are believed to exist. The results conﬁrmed that
there were two patterns of seasonal variation and four patterns of trend
variation in the impression of fashion in general, and eight universal
impressions were extracted.
Keywords: Text mining · Time series · Kansei evaluation index

1Introduction

In recent years, with the diversiﬁcation of user needs and preferences, products
have attracted attention, not only for their value in conventional manufacturing,as with functions and prices but also for their sensibility values, such as usability and comfort [1,2]. The Kansei engineering approach is recognized as themost reliable and eﬀective method to handle aﬀective needs and can be applied
to various domains [3]. One speciﬁc approach to model Kansei evaluation that
expresses the relationship between physical features and impressions of products [4–6]. It is expected that clarifying a user’s aﬀective evaluation (impressionc Springer Nature Switzerland AG 2020
C. Stephanidis and M. Antona (Eds.): HCII 2020, CCIS 1226, pp. 722–729, 2020.
https://doi.org/10.1007/978-3-030-50732-9_92

Extracting Kansei Evaluation Index Using Time Series Text Data723and emotion) of products and providing feedback to their design will improve
product’s quality and brand image. These studies are based on the assumptionthat people’s impressions of products or services is constant. However, some of
the impressions that compose the model include seasonal variation and trend
variation; there may be a mixture of those aﬀected by time-series eﬀects andthose used universally within a certain period.
As an example of analyzing time-series eﬀects of value, there are some studies
about grasping long-term trend information on items and coordinates in thefashion ﬁeld. With the development of artiﬁcial intelligence, research is being
conducted actively to predict the future of fashion trends using machine learning
tasks [7,8]. As the main focus of these studies is on the future of fashion trends,modeling Kansei evaluation quantitatively is not considered.
Therefore, in this study, we work on the classiﬁcation of the Kansei evaluationindexes according to whether it changes chronologically, based on the time-serieschange of the word appearance frequency. In this study, we assume seasonalvariation and trend variation as time-series eﬀects, and classify universality andtemporality based on how they are aﬀected.

2

Previous Study

In a study to quantitatively model the user’s impression using text mining [5,6],a hierarchical structure, consisting of three layers, is assumed: (1) “Emotion”
layer that expresses the preference of the product, (2) “Impression” layer that
expresses the characteristics of the product, and (3) “Physical element” layerthat expresses the physical features of the product. According to this hierarchical structure, the impression structure of the target product ﬁeld is clariﬁed fromthe product review text. Speciﬁcally, to model the impression structure, words
representing the evaluation of the target product (evaluation words) are divided
into emotions and impressions, using multiple dictionary information, and topics
are extracted for the evaluation words corresponding to the impressions (impression words). However, the user’s impressions are thought to be inﬂuenced by timeseries, such as seasonal variation and trend variation. As these factors are notconsidered in the modeling, we must model the impression structure separately
by time-series.
In the case of predicting long-term trends in the fashion ﬁeld, there are studies to discover trends in realway using the similarity of coordinated images byinvestigating the relationship between cutting-edge fashion “runway” and general fashion “realway” in fashion shows [7] and to predict the future popularityof styles discovered from fashion images taken in an unsupervised manner [8].
In these studies, time-series modeling of the visual feature of fashion style ispossible, but the structure of the impression that causes the trend of the visualfeature is not known.

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Methods

We classify the Kansei evaluation indexes according to whether it changes
chronologically, based on time-series changes in the appearance frequency ofevaluation words.
The method consists of four steps. First, we collect and select evaluation
words, using word classes and Japanese dictionaries of evaluation expressions.
Second, we estimate the state-space model that decomposes the appearance ofevaluation words into each factor component (seasonal variation and trend variation). Third, we classify the appearance of evaluation words by clustering eachfactor component. Finally, we cluster evaluation words with small time-serieschanges using embedding words. In this method, we classify the universal Kansei evaluation indexes according to whether it changes chronologically.
The state-space model in this study is indicated by the following formula:yt = μt + γt + t

(1)μt = μt−1 + β + ηt(2)where yt is the evaluation word appearance probabilities vector at time t, μt
is the trend component, γt is the seasonal component with the so-called thefour seasons of spring, summer, autumn and winter (In this study, according to
the four seasons of the Japan Meteorological Agency, spring is from March toMay, summer from June to August, autumn from September to November,andwinter from December to February), t is the irregular and t ∼ N 0, ση2 , μt
is a generalization of the intercept term that can vary dynamically across time,
β is a generalizationof the time-trend that is constant, ηt is the irregular andηt ∼ N 0, ση2 . The seasonal component is modeled as:
γt = −s−1

γt+1−j + ωt

(3)

j=1



where ωt is the irregular and ωt ∼ N 0, ση2 .
yt is calculated for each year and the four seasons and standardized to N (0, 1)
for each evaluation word. This model shows what factor yt is decomposed into
and that it changes in x year. In addition, the state-space model consists of
two equations: an observation Eq. (1) that determines how an internal state isobserved from a certain internal state, and a state Eq. (2) that determines how
the internal state changes with time [9].
The clustering of seasonal variation γt and potential internal state transition
μt by k-means method is used to classify the time-series variation of evaluation
words. By modeling the time-series relationship of the appearance frequency andbundling the evaluation words with similar models, it can be estimated as anevaluation index with the same time-series tendency.
In the classiﬁcation results obtained by the above processing, the time-series
variation can be interpreted, but evaluation words with diﬀerent meanings areExtracting Kansei Evaluation Index Using Time Series Text Data725

mixed into the cluster, making them diﬃcult to interpret. For this reason, weinterpret each cluster using the distributed representation of words and hierarchical clustering. Word2Vec [10] is used as a learning method for the distributedrepresentation of words. Word2Vec is a technique to obtain a distributed representation of words, using a two-layer neural network, called a Skip-gram model.
Next, we calculate the cosine similarity between the evaluation words in eachcluster, using the distributed expression of the evaluation words obtained by
Word2Vec, calculate the distance based on the cosine similarity, and perform
hierarchical clustering by the longest distance method. The number of clusters
in this study is determined, such that the evaluation words with a cosine distancein the top 30% of the whole belong to the same cluster.

4Experiment

4.1

Data

The data included in this study are fashion news articles written at Fashion
Press1 between March 2012 and November 2019, 20,357 pages. There is a structure in which fashion trends are transmitted from top designers and collections in
a top-down fashion. Therefore, we analyze fashion news articles that are locatedin a position that connects users and top designers. Fashion Press has seven categories of news articles: fashion, beauty, gourmet, art, ﬁlm, music, and lifestyle.
In this study, we perform analysis focusing on time-series features in fashion
categories.
4.2

The Collection and Selection of Evaluation Words

To extract the evaluation words necessary to extract the evaluation index in thefashion ﬁeld, we collected and selected evaluation words suitable for expressions
to evaluate fashion. First, to extract evaluation words related to the impression,
the words with a POS that is a main adjective or noun (adjective base) arecollected from all words after morphological analysis. Next, evaluation wordsof other POS are collected using the following three Japanese dictionaries: (1)
EVALDIC ver. 1.0.1 [11,12], (2) Japanese Sentiment Dictionary (Volume ofVerbs and Adjectives) ver. 1.0 [11], and (3) Japanese Sentiment Dictionary (Volume of Nouns) ver. 1.0 [13]. The conditions of the collection are as follows: (1)included in EVALDIC; (2) included in the words given the “evaluation” tagin the Japanese Sentiment Dictionary (Volume of Verbs and Adjectives); (3)included in the words given the “evaluation” tag in the Japanese Sentiment Dictionary (Volume of Nouns); and (4) appear in the fashion category more than
0.5% higher than those in the non-fashion categories.
In a related study [5], evaluation terms are classiﬁed based on the JapaneseAppraisal Dictionary-attitudinal evaluation ver. 1.0. [14] which category information of evaluation expressions is attached. In this dictionary, there are two
1

https://www.fashion-press.net/.

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categories of evaluation expressions: “externalized evaluation” and “internalized evaluation”. Internalized evaluation expresses the evaluator’s feelings andfeelings for the evaluation target, such as “happy” or “fun” and externalized
evaluation indicates the characteristics of the evaluation target, such as “soft”
or “beautiful.” In this study, as in the previous study, evaluation words corresponding to externalized evaluation were selected to clarify the impression
factors that lead to the user’s emotions.
As a result, 117 evaluation words were selected as evaluation words suitablefor expressions that evaluate fashion.
4.3

Time Series Clustering Using State Space Model

As some evaluation words are strongly inﬂuenced by trends and seasonal ﬂuctuations, we classify the evaluation indices using a state-space model and thek-means method.
The parameter estimation of the models described in Sect. 3 is performed by
the BFGS method, and the potential internal state transition μt and seasonalvariation γt obtained from those models are clustered. The optimal number ofclusters was determined using AIC (Akaike Information Criterion). As a result,the number of clusters of potential internal state transition was 4, the number ofclusters of seasonal variation was 2, and evaluation indexes with time-series tendency of all 8 patterns were extracted. Figures 1 and 2 show the cluster centroidsof potential internal state transitions and seasonal variations, respectively.

Fig. 1. Potential internal state transition

Fig. 2. Seasonal variation

Looking at Fig. 1, it is possible to estimate that there are clusters 1 and 2,
which have relatively small ﬂuctuations and are relatively stationary, as well ascluster 3, which has an upward trend, and cluster 4, which has a downward
trend. Looking at Fig. 2, it is possible to interpret cluster 1, which has a strong
seasonal variation, and others.

Extracting Kansei Evaluation Index Using Time Series Text Data

4.4727

Interpretation of Evaluation Index by Word2VecClustering was performed on the evaluation words belonging to cluster 2, which
is a group of evaluation words with small seasonal variation in Fig. 2, among therelatively stationary clusters 1 in Fig. 1. Table 1 shows the results of these interpretations. It was possible to extract eight universal evaluation indices, except
for clusters containing fashion-speciﬁc words like cluster 3 and clusters with only
one word in the cluster, such as cluster 10, which are diﬃcult to interpret.
The clusters obtained from this result are: (1) impression on services andassortment (Clusters 1, 2); (2) impression on physical characteristics (Cluster4); (3) impression on comfort ( Clusters 5, 7); (4) tactile impressions (Cluster6); and (5) visual impressions (Clusters 8, 9). It was possible to interpret allclusters as a set of evaluation words related to fashion, so reasonable results
were considered to have been obtained.
Table 1. Interpretation result

From these results, we extracted eight types of evaluation indices (sense ofsecurity, richness, feeling of size, feeling of wearing, soft feeling, active feeling,

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presence, and pop feeling) that are commonly used in fashion during the targetperiod.

5Conclusion

In this study, we propose a method to classify the Kansei evaluation index, whichis inﬂuenced by time series, from the fashion news articles existing on the web
and the Kansei evaluation index, which is universal within a certain period. In
the proposed method, evaluation words were ﬁrst collected and selected, basedon the category information and the evaluation expression dictionary. Next, wemodeled the time-series changes in the appearance frequency of evaluation words
using the state space method, and clustered seasonal changes γt and potential
internal state transitions μt using the k-means method. Finally, the validity ofthis method was veriﬁed by interpreting the evaluation index using Word2Vec.
In this experiment, we analyzed fashion news articles for 9 years and applied
this method. As a result, it was conﬁrmed that there were two patterns of seasonal variation and four patterns of trend variation in the overall impressionof fashion. We extracted clusters with small ﬂuctuations in internal state andsmall seasonal ﬂuctuations, and performed clustering to interpret their semanticinformation. As a result, we were able to conﬁrm the evaluation indices that are
universally used in the evaluation of eight fashions.
Future tasks include improving the accuracy of the state space model, modeling the relationship with emotions and improving the diversity and usefulnessof the information obtained by investigating clusters that were not used in theinterpretation in Figs. 1 and 2.

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