Next Article in Journal
A Privacy-Preserving Protocol for Utility-Based Routing in DTNs
Previous Article in Journal
The Design and Application of Game Rewards in Youth Addiction Care
Article Menu

Export Article

Open AccessArticle
Information 2019, 10(4), 127; https://doi.org/10.3390/info10040127

Learning Improved Semantic Representations with Tree-Structured LSTM for Hashtag Recommendation: An Experimental Study

, and *
College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Received: 20 January 2019 / Revised: 24 March 2019 / Accepted: 3 April 2019 / Published: 6 April 2019
  |  
PDF [1073 KB, uploaded 23 April 2019]
  |  

Abstract

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. View Full-Text
Keywords: hashtag recommendation; syntactic information; Tree-LSTM hashtag recommendation; syntactic information; Tree-LSTM
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Zhu, R.; Yang, D.; Li, Y. Learning Improved Semantic Representations with Tree-Structured LSTM for Hashtag Recommendation: An Experimental Study. Information 2019, 10, 127.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Information EISSN 2078-2489 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top