{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T14:22:57Z","timestamp":1773325377957,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2019,4,6]],"date-time":"2019-04-06T00:00:00Z","timestamp":1554508800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Undergraduate Innovation and Entrepreneurship Training Program of China","award":["201810225182"],"award-info":[{"award-number":["201810225182"]}]},{"name":"National Natural Science Foundation of China (NSFC)","award":["61806049"],"award-info":[{"award-number":["61806049"]}]},{"name":"Natural Science Foundation of Heilongjiang Province of China","award":["F2018001"],"award-info":[{"award-number":["F2018001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/info10040127","type":"journal-article","created":{"date-parts":[[2019,4,8]],"date-time":"2019-04-08T11:54:52Z","timestamp":1554724492000},"page":"127","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Learning Improved Semantic Representations with Tree-Structured LSTM for Hashtag Recommendation: An Experimental Study"],"prefix":"10.3390","volume":"10","author":[{"given":"Rui","family":"Zhu","sequence":"first","affiliation":[{"name":"College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Delu","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1080\/01969722.2017.1418724","article-title":"Hashtag Recommendation Approach Based on Content and User Characteristics","volume":"49","author":"Tran","year":"2018","journal-title":"Cybern. Syst."},{"key":"ref_2","unstructured":"(2019, April 04). Twitter. Available online: https:\/\/www.twitter.com."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Efron, M. (2010, January 19\u201323). Hashtag retrieval in a microblogging environment. Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Geneva, Switzerland.","DOI":"10.1145\/1835449.1835616"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1504\/IJWS.2012.052535","article-title":"Query expansion for microblog retrieval","volume":"1","author":"Bandyopadhyay","year":"2012","journal-title":"Int. J. Web. Sci."},{"key":"ref_5","unstructured":"Davidov, D., Tsur, O., and Rappoport, A. (2010, January 23\u201327). Enhanced sentiment learning using twitter hashtags and smileys. Proceedings of the 23rd International Conference on Computational Linguistics: Posters, Beijing, China."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wang, X., Wei, F., Liu, X., Zhou, M., and Zhang, M. (2011, January 24\u201328). Topic sentiment analysis in twitter: A graph-based hashtag sentiment classification approach. Proceedings of the 20th ACM International Conference on Information and Knowledge Management, Glasgow, UK.","DOI":"10.1145\/2063576.2063726"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Li, Y., Jiang, J., Liu, T., and Sun, X. (2015, January 16\u201317). Personalized Microtopic Recommendation with Rich Information. Proceedings of the 4th National Conference on Social Media Processing (SMP 2015), Guangzhou, China.","DOI":"10.1007\/978-981-10-0080-5_1"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Hong, L., Ahmed, A., Gurumurthy, S., Smola, A.J., and Tsioutsiouliklis, K. (2012, January 16\u201320). Discovering Geographical Topics in the Twitter Stream. Proceedings of the 21st International Conference on World Wide Web, Lyon, France.","DOI":"10.1145\/2187836.2187940"},{"key":"ref_9","unstructured":"Zangerle, E., Gassler, W., and Specht, G. (2011, January 15). Recommending#-tags in twitter. Proceedings of the 2nd International Workshop on Semantic Adaptive Social Web (SASWeb 2011), Girona, Spain."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Godin, F., Slavkovikj, V., De Neve, W., Schrauwen, B., and Van de Walle, R. (2013, January 13\u201317). Using Topic Models for Twitter Hashtag Recommendation. Proceedings of the 22nd International Conference on World Wide Web, Rio de Janeiro, Brazil.","DOI":"10.1145\/2487788.2488002"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kim, Y. (2014, January 25\u201329). Convolutional Neural Networks for Sentence Classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar.","DOI":"10.3115\/v1\/D14-1181"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Tai, K.S., Socher, R., and Manning, C.D. (2015). Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks. arXiv.","DOI":"10.3115\/v1\/P15-1150"},{"key":"ref_14","unstructured":"(2019, April 04). yangdelu855\/Tree-LSTM. Available online: https:\/\/github.com\/yangdelu855\/Tree-LSTM."},{"key":"ref_15","unstructured":"Mazzia, A., and Juett, J. (2009). Suggesting Hashtags on Twitter, Computer Science and Engineering, University of Michigan. EECS 545m: Machine Learning."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Krestel, R., Fankhauser, P., and Nejdl, W. (2009, January 23\u201325). Latent Dirichlet Allocation for Tag Recommendation. Proceedings of the Third ACM Conference on Recommender Systems (RecSys \u201909), New York, NY, USA.","DOI":"10.1145\/1639714.1639726"},{"key":"ref_17","first-page":"993","article-title":"Latent Dirichlet Allocation","volume":"3","author":"Blei","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1108\/00220410410560582","article-title":"Understanding inverse document frequency: On theoretical arguments for IDF","volume":"60","author":"Robertson","year":"2004","journal-title":"J. Doc."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/5254.708428","article-title":"Support vector machines","volume":"13","author":"Hearst","year":"1998","journal-title":"IEEE Intell. Syst. Their Appl."},{"key":"ref_20","unstructured":"Li, Y., Liu, T., Jiang, J., and Zhang, L. (2016, January 11\u201316). Hashtag Recommendation with Topical Attention-Based LSTM. Proceedings of the 26th International Conference on Computational Linguistics, Osaka, Japan."},{"key":"ref_21","unstructured":"Gong, Y., and Zhang, Q. (2016, January 9\u201315). Hashtag Recommendation Using Attention-Based Convolutional Neural Network. Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI 2016), New York, NY, USA."},{"key":"ref_22","first-page":"1137","article-title":"A neural probabilistic language model","volume":"3","author":"Bengio","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_23","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., and Dean, J. (2013, January 5\u201310). Distributed representations of words and phrases and their compositionality. Proceedings of the Advances in Neural Information Processing Systems (NIPS 2013), Lake Tahoe, NV, USA."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Joulin, A., Grave, E., Bojanowski, P., and Mikolov, T. (2017, January 3\u20137). Bag of Tricks for Efficient Text Classification. Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, Valencia, Spain.","DOI":"10.18653\/v1\/E17-2068"},{"key":"ref_25","unstructured":"Chen, Q., Zhu, X., Ling, Z., Wei, S., and Jiang, H. (August, January 30). Enhancing and Combining Sequential and Tree LSTM for Natural Language Inference. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, BC, Canada."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Chen, D., and Manning, C. (2014, January 25\u201329). A Fast and Accurate Dependency Parser using Neural Networks. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar.","DOI":"10.3115\/v1\/D14-1082"},{"key":"ref_27","unstructured":"Greg, D., and Dan, K. (2015, January 26\u201331). Neural CRF Parsing. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, Beijing, China."},{"key":"ref_28","unstructured":"Che, W., Li, Z., and Liu, T. (2010, January 23\u201327). LTP: A Chinese Language Technology Platform. Proceedings of the 23rd International Conference on Computational Linguistics, Beijing, China."},{"key":"ref_29","unstructured":"Ji, F., Gao, W., Qiu, X., and Huang, X. (2013, January 4\u20139). FudanNLP: A Toolkit for Chinese Natural Language Processing with Online Learning Algorithms. Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, Sofia, Bulgaria."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Yang, J., and Leskovec, J. (2011, January 9\u201312). Patterns of Temporal Variation in Online Media. Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, Hong Kong, China.","DOI":"10.1145\/1935826.1935863"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.neucom.2017.06.056","article-title":"Hashtag recommendation for multimodal microblog posts","volume":"272","author":"Gong","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_32","unstructured":"(2019, April 04). Zhihu. Available online: https:\/\/www.zhihu.com\/topics."},{"key":"ref_33","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_34","unstructured":"Ding, Z., Zhang, Q., and Huang, X. (2012, January 8\u201315). Automatic hashtag recommendation for microblogs using topic-specific translation model. Proceedings of the 24th International Conference on Computational Linguistics, COLING 2012, Mumbai, India."},{"key":"ref_35","first-page":"337","article-title":"On Recommending Hashtags in Twitter Networks","volume":"Volume 7710","author":"Aberer","year":"2012","journal-title":"Social Informatics, Proceedings of the 4th International Conference, SocInfo 2012, Lausanne, Switzerland, 5\u20137 December 2012"},{"key":"ref_36","first-page":"610","article-title":"What to Tag Your Microblog: Hashtag Recommendation Based on Topic Analysis and Collaborative Filtering","volume":"Volume 8709","author":"Chen","year":"2014","journal-title":"Web Technologies and Applications, Proceedings of the 16th Asia-Pacific Web Conference, APWeb 2014, Changsha, China, 5\u20137 September 2014"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.future.2015.10.012","article-title":"A personalized hashtag recommendation approach using LDA-based topic model in microblog environment","volume":"65","author":"Zhao","year":"2016","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Li, Q., Shah, S., Nourbakhsh, A., Liu, X., and Fang, R. (2016, January 24\u201328). Hashtag Recommendation Based on Topic Enhanced Embedding, Tweet Entity Data and Learning to Rank. Proceedings of the 25th ACM International Conference on Information and Knowledge Management (CIKM \u201916), Indianapolis, IN, USA.","DOI":"10.1145\/2983323.2983915"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ma, Z., Sun, A., Yuan, Q., and Cong, G. (2014, January 3\u20137). Tagging Your Tweets: A Probabilistic Modeling of Hashtag Annotation in Twitter. Proceedings of the 23rd ACM International Conference on Information and Knowledge Management (CIKM \u201914), Shanghai, China.","DOI":"10.1145\/2661829.2661903"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/MIS.2015.20","article-title":"A Twitter Hashtag Recommendation Model that Accommodates for Temporal Clustering Effects","volume":"30","author":"Lu","year":"2015","journal-title":"IEEE Intell. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Kowald, D., Pujari, S.C., and Lex, E. (2017, January 3\u20137). Temporal Effects on Hashtag Reuse in Twitter: A Cognitive-Inspired Hashtag Recommendation Approach. Proceedings of the 26th International Conference on World Wide Web Companion (WWW\u201917), Perth, Australia.","DOI":"10.1145\/3038912.3052605"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Wang, J., Huang, H., Huang, X., and Gong, Y. (2017, January 19\u201325). Hashtag Recommendation for Multimodal Microblog Using Co-Attention Network. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17), Melbourne, Australia.","DOI":"10.24963\/ijcai.2017\/478"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/10\/4\/127\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:43:27Z","timestamp":1760186607000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/10\/4\/127"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,6]]},"references-count":42,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2019,4]]}},"alternative-id":["info10040127"],"URL":"https:\/\/doi.org\/10.3390\/info10040127","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,4,6]]}}}