Pan-Pacific Conference XXXIV. Designing New Business Models in Developing Economies

social media with short sentences of up to 200
characters and anyone can freely write it
comfortably. Furthermore, it is a new form of chat
through computer and is a real-time platform that
does not screen rigorous information based on the
user's social relationships strict mechanism 13); In
emergencies like disasters and various crisis
situations, Twitter can be a source of information
for users participating in communication to
immediately judge the situations in which they are
located 15);
3. Big data processing of anxiety
A corpus based discourse analysis [2][3] is
conducted to evaluate anxiety on a disaster risk
which addressed by the government agency
announcement (Tokyo Electric Power) and
Twitter[4]. In detail, i) annotation, ii) TFIDF
analysis, iii) extraction of co-occurrences on topic
"radiation", iv) evaluation of anxiety polarity, and v)
definition of anxiety index, and vi) analysis of time
series change of anxiety were conducted.
In this study, the word emotion polarity
correspondence table developed by Takamura et al.
[5] is used to determine the emotional polarity of
the evaluation on a risk event “radiation” that is
closely related to the nuclear power accident of the
311 Great East Japan Earthquake. For example, if
assuming that "radiation" is a risk expression word
for the sentence "I am afraid that radiation will
come to the prefectural south", the co-occurrence of
"afraid" which is a co-occurrence of "radiation" and
calculate the degree of anxiety by multiplying the
frequency by the emotional polarity 0.99799. As a
result, it is possible to analyze differences in
polarity judgment between disasters and normal
times for the same risk event.
However, in Twitter communication, there are many
expressions that are not structured as sentences, and
there is a possibility that the word emotion polarity
correspondence table cannot be simply applied.
Therefore, instead of using the word emotion
polarity correspondence table as it is, this study use
it as a criterion for judging whether the evaluation
word is negative or not. If it is possible to visualize
that people's emotional polarity to risk events, that
is, the negative degree of evaluation changes
significantly in Twitter, it will be evaluated that the
emotional polarities of people at disaster and normal
times change. Therefore, it is possible to analyze the
extent and timing of anxiety spread among people
as the situation after the disaster develops.
the occurrence of accidents. During the first half of
the data collection period, the timing of the rapid
increase in co-occurrence frequency was almost six
hours after the nuclear accidents, while the
frequency rose sharply three hours later during the
latter half of the data collection period. As noted in
above, people directed more of their attention to
‘radiation’ after the sequence of nuclear accidents
on March 15. When assessing risks, people pay
attention to risks sensitively and try to solve current
problems with limited information obtained by the
attention which is also scarce.
Figure 1. The Time Series Variation of Co-
Occurrence Frequency with ‘radiation’ in Twitter
To measure the level of anxiety using the crisis
communication corpus, an Anxiety Index has been
proposed that refers to the degree to which a risk
(e.g., earthquake, tsunami, or radiation) is perceived
negatively. The proposed Anxiety Index is defined
with Equation 1.
COF
Anxiety
TF
N
(1)
4. Analysis results
Figure 1 shows the time series variation of total cooccurrence
frequency with ‘radiation’ and only
negative co-occurrence frequency in Twitter corpus.
The co-occurrence frequency had been documented
every three hours for an hour at a time. Just after the
disaster, co-occurrence frequency increased
abruptly until 9 p.m. and declined dramatically three
hours later (midnight). The volume of risk
assessment including ‘radiation’ changed Fig. 1.
The Time Series Variation of Co-Occurrence
Frequency with ‘radiation’ in Twitter according to
Fig. 2 The Time Series Variation of Anxiety
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