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- W2766098587 abstract "This research work tries to understand the “interest” of the users of Twitter, where we define the interest as the maximum number of tweets a user has posted on a single topic. The discovery of top interest of for each Twitter user can have a variety of applications at Twitter including user recommendation with same interests and advertiser tagging. It would also result in a rich user experience. Although the discovery of finding similar users on Twitter is a well-studied problem, this work tries to achieve that result by building an interest matrix for each user from their tweets and thus including an important variable in finding similar users. This interest matrix is achieved using topic modelling. In real world, it is safe to assume that the more a person talks about something, the more they are interested about it. The algorithm tries to replicate the same assumption on Twitter where it tries to detect the parent topic of each tweet (e.g. sports, politics, science) of the user and then would segregate them as different interest." @default.
- W2766098587 created "2017-11-10" @default.
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- W2766098587 date "2017-05-01" @default.
- W2766098587 modified "2023-09-27" @default.
- W2766098587 title "Pattern recognition of Twitter users using semantic topic modelling" @default.
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- W2766098587 doi "https://doi.org/10.1109/iciota.2017.8073636" @default.
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