Asia Trend Map: Forecasting “Cool Japan” Content Popularity on Web Data
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This document discusses a system called Asia Trend Map that forecasts the popularity of Japanese content such as anime, manga, and games in Asian countries over the next 6 months. The system collects data on these cultural products from Twitter, Wikipedia, and search engines in different Asian languages and uses this web data along with past Japanese sales data to train a model that can predict future trends. The results showed the system could improve its predictive accuracy by combining data sources and that Wikipedia data, especially page content attributes, was particularly helpful for predicting longer term trends.
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Asia Trend Map: Forecasting “Cool Japan” Content Popularity on Web Data
- 1. Asia Trend Map: Forecasting “Cool Japan” Content Popularity on Web Data Shuhei Iitsuka The University of Tokyo Ohma Inc. 2013/08/20 1
- 2. 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. 2013/08/20 2 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.
- 3. 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 e?ect –? Growing market ?? Why web data? –? Unauthorized copies are widely distributed around the country à? There’s di?culty in catching the trend from the sales data 2013/08/20 3 ?
- 4. Demonstration: Asia Trend Map ?? This system can forecast about 4,000 Japanese content’s popularity trends following 6 months for 13 countries in Asia. 2013/08/20 4
- 5. Model Overview 2013/08/20 5 Twitter Wikipedia search ?engine web ? data forecasting ? model consumer, ? user ? tweet edit search crawl crawl crawl attribute ? extraction training ?data ? (Sales ?in ?Japan) SVR
- 6. 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 ... 2013/08/20 6 jp.wikipedia.org zh.wikipedia.org ko.wikipedia.org NARUTO ??? 火影忍者 Jump ? (Magazine) Ramen Forward ?Link Backward ?Link International ?Link Wikipedia ?link ?example:
- 7. 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. 2013/08/20 7 NARUTO Wikipedia 火影忍者 ??? Twitter Search ? Engine T_(m, ?China) T_(m, ?Korea) S_(m, ?China) S_(m, ?Korea)
- 8. 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. 2013/08/20 8
- 9. 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. 2013/08/20 9
- 10. 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. 2013/08/20 10
- 11. 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. 2013/08/20 11