Regional Information Video Searches Using Word Searches Generated by Twitter Posts
Regional Information Video Searches Using Word Searches Generated by Twitter Posts
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使用 Twitter 帖子生成的单词搜索进行区域信息视频搜索
DOI：
10.1109/iiai-aai.2015.256
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
and Hiromitsu shiina
中科院分区：
文献类型：
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作者：
Masahiro TAKEDA;Nobuyuki KOBAYASHI;Fumio KITAGAWA;and Hiromitsu shiina
目前有许多服务可在互联网上，包括地图搜索网站，如雅虎！地图和视频搜索网站，如YouTube。但是，从这些服务中可以获得的信息是有限的。例如，在地图搜索网站上，您可以在地图上找到一个机构的位置，但是，您无法获得有关该机构本身的信息或查看在那里举行的活动的视频。另一方面，虽然你可以在视频网站上找到视频，但很难找到视频拍摄的地点。此外，旅游信息网站能够提供图像和位置等信息，但它们提供的事件视频或有关该机构的详细信息很少，限制了您在任何时候都能够获得的信息总量。因此，我们的系统是基于结合多种服务以获得许多不同类型的有用信息的想法。然而，基于仅使用设施名称的搜索，搜索结果的完整性低。通过从与搜索到的设施相关的Twitter帖子中提取与设施相关的词，并使用该词，搜索结果有许多变化。为了从Twitter帖子中提取与设施相关的词，预先使用基于机器学习的分类的结果来确定推文是否与设施相关。
There are currently many services available on the Internet including map search sites such as Yahoo! Map, and video search sites such as YouTube. However, there is a limit to the information that can be obtained from each of these services. For example, on map search sites you can find the location of an establishment on a map, however, you cannot obtain information about the establishment itself or view videos of events being held there. On the other hand, although you can find videos on video sites, it is then difficult to find out the locations where the videos were taken in the first place. In addition, tourist information sites are able to provide information such as images and locations, but they offer few videos of events or detailed information about the establishment, limiting the overall amount of information that you are able to obtain at any one time. Our system is, therefore, based on the idea of combining multiple services to obtain many different types of useful information. However, completeness of the search results is low based on a search only using the facility name. By extracting words related to facilities from Twitter posts related to the searched facility and also using that word, many variations to the search results were given. In order to extract words related to facilities from Twitter posts, the results of a machine based learning classification were used in advance to determine whether or not the Tweet relates to a facility.