A hashtag is a type of metadata tag used on social networks and can help people search for specific topics or content. To capture the interactive information between words and understand the content of microblog posts deeply, this study proposed a neural network model based on a word-level self-attention mechanism. Given a microblog post, the weight of each word was calculated through a self-attention mechanism, and then the representation of a microblog post was obtained through the weighted summation of words. Finally, a multi-layer perceptron was adopted on the representation of a microblog post to perform the classification. The effectiveness of the proposed model was verified through experiments of large-scale datasets. Results show that: (1) introducing word-level self-attention mechanism into hashtag recommendation is effective. (2) In comparison with the baseline methods used in previous studies, such as convolutional neural network or long short-term memory network, the proposed self-attentive neural networks can provide a more accurate representation of a microblog post and significantly improve the F-score of hashtag recommendation on the same dataset. This study provides references for the methods and evaluation of short-text hashtag recommendations, such as microblogs.
A hashtag is a type of metadata tag used on social networks, such as Twitter and other microblogging services. Hashtags indicate the core idea of a microblog post and can help people to search for specific themes or content. However, not everyone tags their posts themselves. Therefore, the task of hashtag recommendation has received significant attention in recent years. To solve the task, a key problem is how to effectively represent the text of a microblog post in a way that its representation can be utilized for hashtag recommendation. We study two major kinds of text representation methods for hashtag recommendation, including shallow textual features and deep textual features learned by deep neural models. Most existing work tries to use deep neural networks to learn microblog post representation based on the semantic combination of words. In this paper, we propose to adopt Tree-LSTM to improve the representation by combining the syntactic structure and the semantic information of words. We conduct extensive experiments on two real world datasets. The experimental results show that deep neural models generally perform better than traditional methods. Specially, Tree-LSTM achieves significantly better results on hashtag recommendation than standard LSTM, with a 30% increase in F1-score, which indicates that it is promising to utilize syntactic structure in the task of hashtag recommendation.
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