Name
Abstract | ||
|---|---|---|
Since the introduction of microblogging services, there has been a continuous growth of short-text social networking on the Internet. With the generation of large amounts of microposts, there is a need for effective categorization and search of the data. Twitter, one of the largest microblogging sites, allows users to make use of hashtags to categorize their posts. However, the majority of tweets do not contain tags, which hinders the quality of the search results. In this paper, we propose a novel method for unsupervised and content-based hashtag recommendation for tweets. Our approach relies on Latent Dirichlet Allocation (LDA) to model the underlying topic assignment of language classified tweets. The advantage of our approach is the use of a topic distribution to recommend general hashtags. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2487788.2488002 | WWW (Companion Volume) |
Keywords | Field | DocType |
effective categorization,largest microblogging site,content-based hashtag recommendation,twitter hashtag recommendation,latent dirichlet allocation,microblogging service,continuous growth,general hashtags,underlying topic assignment,topic model,search result,topic distribution,topic models | Categorization,Data mining,Latent Dirichlet allocation,World Wide Web,Social media,Social network,Computer science,Microblogging,Topic model,The Internet | Conference |
ISBN | Citations | PageRank |
978-1-4503-2038-2 | 64 | 2.13 |
References | Authors | |
3 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fréderic Godin | 1 | 102 | 10.62 |
| Viktor Slavkovikj | 2 | 79 | 5.41 |
| Wesley De Neve | 3 | 525 | 54.41 |
| Benjamin Schrauwen | 4 | 1686 | 95.82 |
| Rik Van de Walle | 5 | 2040 | 238.28 |