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The hash symbol, called a hashtag, is used to mark the keyword or topic in a tweet. It was created organically by users as a way to categorize messages. Hashtags also provide valuable information for many research applications such as sentiment classification and topic analysis. However, only a small number of tweets
, keyword extraction and similarity search in the broad fields of text mining, information retrieval, statistical language modeling. In this work, a dataset with 200 abstracts fall under four topics are collected from two different domain journals for tagging journal abstracts. The document models are built using LDA (Latent
receiving. Since messages posted by microblogging users reflect their interests, and the keywords in the messages indicate their main focus to a large extent, we can discover users’ preferences by analyzing the user-generated contents. Moreover, users’ interests are not static, on the contrary, they change as
The wide diffusion of community tagging sites and related folksonomies has made the knowledge discovery and retrieval still much more urgent topic. If tagging systems allow users to add freely keywords to web resources, clicking on a tag has the side effect of a tag-based query, since enables the users to explore
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.