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by the network -- descriptive keywords, or tags. In this paper we present a model that enables keyword discovery methods through the interpretation of the network as a graph, solely relying on keywords that categorize or describe productive items. The model and keyword discovery methods presented in this paper avoid
, tornadoes, earthquakes), or incidents (e.g., traffic jams). However, current search on social media data is mostly keyword or hashtag based. The keyword based search method does not allow efficient search of events. In order to detect, scan, and search location based events from social media, users and social texts need to be
As more web services are offered on the Web, it is becoming increasingly difficult for users to manage and search for online content, using only flat keyword searching. Users often forget how they tagged their data but may remember generic information such as the location they were in when they took the picture. We
We analyze email communications within a large company to reveal how email activity patterns depend on content. We characterize email contents using keywords and examine statistics of email transmissions. As a result, we are able to identify differences in network structures and propagation behaviors depending on the
generalized concepts representation of text (1) overcomes surface level differences (which arise when different keywords are used for related concepts) without drift, (2) leads to a higher-level semantic network representation of related stories, and (3) when used as features, they yield a significant 36% boost in performance
to collect active commercial entities and a commercial relation lexicon is created to collect keywords that flag commercial relations. Illustration and applications are also discussed, which undoubtedly discloses a promising future of commercial network study.
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