Hashtag recommendation for short social media texts using word-embeddings and external knowledge

With the rapid growth of Twitter in recent years, there has been a tremendous increase in the number of tweets generated by users. Twitter allows users to make use of hashtags to facilitate effective categorization and retrieval of tweets. Despite the usefulness of hashtags, a major fraction of tweets do not contain hashtags. Several methods have been proposed to recommend hashtags based on lexical and topical features of tweets. However, semantic features and data sparsity in tweet representation have rarely been addressed by existing methods. In this paper, we propose a novel method for hashtag recommendation that resolves the data sparseness problem by exploiting the most relevant tweet information from external knowledge sources. In addition to lexical features and topical features, the proposed method incorporates the semantic features based on word-embeddings and user influence feature based on users’ influential position. To gain the advantage of various hashtag recommendation methods based on different features, our proposed method aggregates these methods using learning-to-rank and generates top-ranked hashtags. Experimental results show that the proposed method significantly outperforms the current state-of-the-art methods.