AAAI Publications, Workshops at the Thirtieth AAAI Conference on Artificial Intelligence

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Discovering Relevant Hashtags for Health Concepts: A Case Study of Twitter
Quanzhi Li, Sameena Shah, Rui Fang, Armineh Nourbakhsh, Xiaomo Liu

Last modified: 2016-03-29

Abstract


Hashtags are useful in many applications, such as tweet classification, clustering, searching, indexing and social network analysis. This study seeks to recommend relevant Twitter hashtags for health-related keywords based on distributed language representations, generated by the state-of-the-art Deep Learning technology. The word embeddings are built from billions of tweet words without supervision. To the best of our knowledge, this is the first study of applying distributed language representations to recommending hashtags for keywords. The experiment showed that this approach outperformed the baseline approach that is based on keyword and hashtag co-occurrence in tweets.

Keywords


hashtag;word2vec;word embedding; social media;twitter;tweet;health keyword;distributed representations of words

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