Takako Hashimoto

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Social media offers a wealth of insight into howsignificant events -- such as the Great East Japan Earthquake, the Arab Spring, and the Boston Bombing -- affect individuals. The scale of available data, however, can be intimidating: duringthe Great East Japan Earthquake, over 8 million tweets weresent each day from Japan alone. Conventional word(More)
Sports videos for mobile terminals and PCs and other multimedia information services have recently spread. The ability to efficiently extract significant scenes is important with these services. We proposed the personal digest making scheme (PDMS), which calculates the significance of events in a sports game based on video meta data and extracts significant(More)
In this paper, we design a new method to explore the social context as a community mapping from a buzz marketing site. In this method, after extracting significant topical terms from messages in buzz marketing sites, first we construct a snapshot co-occurrence network at each time stamp. Next, we organize topic hierarchical structures from each(More)
Feature selection is a useful tool for identifying which features, or attributes, of a dataset cause or explain phenomena, and improving the efficiency and accuracy of learning algorithms for discovering such phenomena. Consequently, feature selection has been studied intensively in machine learning research. However, advanced feature selection algorithms(More)