Hashtag recommendation based on user tweet and hashtag classification on twitter

Mina Jeon, Sanghoon Jun, Een Jun Hwang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)

Abstract

With the explosive popularity of various social network services (SNSs), an enormous number of user documents are generated and shared daily by users. Considering the volume of user documents, efficient methods for grouping or searching relevant user documents are required. In the case of Twitter, self-defined metadata called hashtags are attached to tweets for that purpose. However, due to the wide scope of hashtags, users are having difficulty in finding out appropriate hashtags for their tweets. In this paper, we propose a new hashtag recommendation scheme for user tweets based on user tweet analysis and hashtag classification. More specifically, we extract keywords from user tweets using TF-IDF and classify their hashtags into pre-defined classes using Naïve Bayes classifier. Next, we select a user interest class based on keywords of user tweets to reflect user interest. To recommend appropriate hashtags to users, we calculate the ranks of candidate hashtags by considering similar tweets, user interest and popularity of hashtags. To show the perfor­mance of our scheme, we developed an Android application named “TWITH” and evaluate its recommendation accuracy. Through various experiments, we show that our scheme is quite effective in the hashtag recommendation.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages325-336
Number of pages12
Volume8597
ISBN (Print)9783319115375
DOIs
Publication statusPublished - 2014 Jan 1
Event36th German Conference on Pattern Recognition, GCPR 2014 - Münster, Germany
Duration: 2014 Sep 22014 Sep 5

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8597
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other36th German Conference on Pattern Recognition, GCPR 2014
CountryGermany
CityMünster
Period14/9/214/9/5

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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  • Cite this

    Jeon, M., Jun, S., & Hwang, E. J. (2014). Hashtag recommendation based on user tweet and hashtag classification on twitter. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8597, pp. 325-336). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8597). Springer Verlag. https://doi.org/10.1007/978-3-319-11538-2_30