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      Twitter Hash Tag Recommendation

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          Abstract

          The rise in popularity of microblogging services like Twitter has led to increased use of content annotation strategies like the hashtag. Hashtags provide users with a tagging mechanism to help organize, group, and create visibility for their posts. This is a simple idea but can be challenging for the user in practice which leads to infrequent usage. In this paper, we will investigate various methods of recommending hashtags as new posts are created to encourage more widespread adoption and usage. Hashtag recommendation comes with numerous challenges including processing huge volumes of streaming data and content which is small and noisy. We will investigate preprocessing methods to reduce noise in the data and determine an effective method of hashtag recommendation based on the popular classification algorithms.

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          An algorithm for suffix stripping

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            Using topic models for Twitter hashtag recommendation

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              Improving tweet stream classification by detecting changes in word probability

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                Information & Library science, Artificial intelligence

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