Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
Toward tighter integration of web search with a geographic information system
Proceedings of the 15th international conference on World Wide Web
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Extracting Concept Hierarchy Knowledge from the Web Based on Property Inheritance and Aggregation
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Mining the web for hyponymy relations based on property inheritance
APWeb'08 Proceedings of the 10th Asia-Pacific web conference on Progress in WWW research and development
Linearly-Combined Web Sensors for Spatio-temporal Data Extraction from the Web
ICDMW '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops
Mining the web for appearance description
DEXA'07 Proceedings of the 18th international conference on Database and Expert Systems Applications
| Hi-index | 0.00 |
Many researches on mining the Web, especially Social Networking Media such as web logs and microblogging sites which seem to store vast amounts of information about human societies, for knowledge about various phenomena and events in the physical world have been done actively, and Web applications with Web-mined knowledge have begun to be developed for the public. However, there is no detailed investigation on how accurately Web-mined data reflect real-world data. It must be problematic to idolatrously utilize the Web-mined data in public Web applications without ensuring their accuracy sufficiently. Therefore, this paper defines spatio-temporal Web Sensors by analyzing Twitter, Facebook, web logs, news sites, or the whole Web for a target natural phenomenon, and tries to validate the potential and reliability of the Web Sensors' spatio-temporal data by measuring the coefficient correlation with Japanese weather, earthquake, and influenza statistics per week by region as real-world data.