Hashtag Recommendation Using Dirichlet Process Mixture Models Incorporating Types of Hashtags

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 401–410, Lisbon, Portugal, 17-21 September 2015. c⃝2015 Association for Computational Linguistics. Hashtag Recommendation Using Dirichlet Process Mixture Models Incorporating Types of Hashtags Yeyun Gong, Qi Zhang, Xuanjing Huang Shanghai Key Laboratory of Data Science School of Computer Science, Fudan University 825 Zhangheng Road, Shanghai, P.R.China {12110240006, qz, xjhuang}@fudan.edu.cn Abstract In recent years, the task of recommending hashtags for microblogs has been given increasing attention. Various methods have been proposed to study the problem from different aspects. However, most of the recent studies have not considered the differences in the types or uses of hashtags. In this paper, we introduce a novel nonparametric Bayesian method for this task. Based on the Dirichlet Process Mixture Models (DPMM), we incorporate the type of hashtag as a hid- den variable. The results of experiments on the data collected from a real world microblogging service demonstrate that the proposed method outperforms state- of-the-art methods that do not consider these aspects. By taking these aspects into consideration, the relative improvement of the proposed method over the state-of-the- art methods is around 12.2% in F1- score. 1 Introduction Hashtags are used to mark keywords or topics in a microblog. Over the past few years, social media services have become some of the most important communication channels for people. According to the statistic reported by the Pew Research Centers Internet & American Life Project in Aug 5, 2013, about 72% of adult internet users are also members of at least one social networking site. Hence, microblogs have also been widely used as data sources for public opinion analy- ses (Bermingham and Smeaton, 2010; Jiang et al., 2011), prediction (Asur and Huberman, 2010; Bollen et al., 2011), reputation management (Pang and Lee, 2008; Otsuka et al., 2012), and many other applications (Sakaki et al., 2010; Becker et al., 2010; Guy et al., 2010; Guy et al., 2013). In addition to the limited number of characters in the content, microblogs also contain a form of metadata tag (hashtag), which is a string of characters preceded by the symbol (#). Hashtags are used to mark the keywords or topics of a microblog. They can occur anywhere in a microblog, at the beginning, middle, or end. Hashtags have been proven to be useful for many applications, including microblog retrieval (Efron, 2010), query expansion (A.Bandyopadhyay et al., 2011), and sentiment analysis (Davidov et al., 2010; Wang et al., 2011). However, only a small percentages of microblogs contain hashtags provided by their authors. Hence, the task of rec- ommending hashtags for microblogs has become an important research topic and has received con- siderable attention in recent years. Existing works have studied discriminative models (Ohkura et al., 2006; Heymann et al., 2008) and generative models (Blei and Jordan, 2003; Krestel et al., 2009; Ding et al., 2013; Godin et al., 2013) based on the textual information of a single microblog. Since microblog users are free to develop and use their own hashtags, …

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