Asia Trend Map: Forecasting “Cool Japan” Content Popularity on Web Data
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Asia Trend Map: Forecasting “Cool Japan” Content Popularity on Web Data Shuhei Iitsuka The University of Tokyo Ohma Inc. 2013/08/20 1
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Brief introduction about Asia Trend Map: "Cool Japan" content popularity forecasting system
Transcript of Asia Trend Map: Forecasting “Cool Japan” Content Popularity on Web Data
Asia Trend Map: Forecasting “Cool Japan” Content Popularity on Web Data Shuhei Iitsuka
The University of Tokyo Ohma Inc.
2013/08/20 1
Background • Anime, Manga and Game has become popular around the world. • Japanese content industries are willing to promote their products
overseas under the brand of “Cool Japan”. • However, localization processes (translation, promoting etc.) take
costs a lot of money and time.
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Japan in London: Sushi, Manga, Cosplay and Camden – visitlondon.com http://blog.visitlondon.com/2010/09/japan-in-london-sushi-manga-cosplay-and-camden/
à Sellers need to estimate the product’s popularity in the target market and allocate their resources strategically.
Purpose • Forecasting each product's popularity around Asian countries
based on web data from Twitter, Wikipedia and a search engine. • Why Asia?
– Close to Japan geographically and culturally à direct economic effect – Growing market
• Why web data? – Unauthorized copies are widely distributed around the country à There’s difficulty in catching the trend from the sales data
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?
Demonstration: Asia Trend Map • This system can forecast about 4,000 Japanese content’s
popularity trends following 6 months for 13 countries in Asia.
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Model Overview
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Wikipedia
search engine
web data
forecasting model
consumer, user
tweet
edit
search
crawl
crawl
crawl
attribute extraction
training data (Sales in Japan)
SVR
Wikipedia Data Attributes • Edit
– Monthly Edit Count, Monthly Unique Editor Count, Average Edit Count Per User ...
• Link – Number of Forward Links, Number of Backward Links ...
• Content – Number of International Links, Page Size, Number of Sections ...
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jp.wikipedia.org
zh.wikipedia.org
ko.wikipedia.org NARUTO
나루토
火影忍者 Jump
(Magazine)
Ramen Forward Link
Backward Link International Link
Wikipedia link example:
month: m
Twitter and Search Engine • Twitter: Extract number of tweets which includes the product
name (monthly) • Search Engine: Extract number of times the product name is
searched (monthly) • We get each product’s local name utilizing Wikipedia database.
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NARUTO
Wikipedia
火影忍者
나루토
Search Engine
T_(m, China)
T_(m, Korea) S_(m, China) S_(m, Korea)
Pre-processing on Training Data • Sales of Manga suddenly increases when new volume is out. à We connect the peak with lines and make use of this as training data.
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Experimental Results • Prediction precision is improved by applying attributes of
multiple web services. • Especially, Wikipedia data took an importance role in predicting
the trends in more distant future.
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Experimental Results • Among the Wikipedia data attributes, Page Content (Number of
international links, Page size, etc.) took the most important role in predicting the trend.
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Conclusion • We built the forecasting system of Japanese cultural products
from web data • We launched a website based on this system: Asia Trend Map • We'd like to contribute to strategic planning process of "Cool
Japan" with this.
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