Hashtag recommendation based on user tweet and hashtag classification on twitter

Mina Jeon, Sanghoon Jun, Eenjun Hwang

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

20 Citations (Scopus)


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 publicationWeb-Age Information Management - WAIM 2014 International Workshops
Subtitle of host publicationBigEM, HardBD, DaNoS, HRSUNE, BIDASYS, Revised Selected Papers
EditorsWolf-Tilo Balke, Jianliang Xu, Peiquan Jin, Tiffany Tang, Xin Lin, Eenjun Hwang, Yueguo Chen, Wei Xu
PublisherSpringer Verlag
Number of pages12
ISBN (Electronic)9783319115375
Publication statusPublished - 2014
Event36th German Conference on Pattern Recognition, GCPR 2014 - Münster, Germany
Duration: 2014 Sept 22014 Sept 5

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other36th German Conference on Pattern Recognition, GCPR 2014


  • Android
  • Classification
  • Hashtag
  • Naïve Bayes classifier
  • Ranking
  • Recommendation
  • Twitter
  • User interest

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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