Skip to main content
SpringerLink
Log in

Dynamic-Keyword Extraction from Social Media

  • Conference paper
  • First Online:
Book cover Advances in Information Retrieval (ECIR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11437))

Included in the following conference series:

  • 2543 Accesses

Abstract

Traditional keyword extraction methods make the assumption that corpora is static. However, in social media, information is highly dynamic, with individual words showing a dynamic behaviour. In this paper we propose an unsupervised approach that jointly models words’ temporal behaviour and keyword’s semantic affinity, to address the task of dynamic-keyword extraction. Experiments show the method effectiveness and confirm the importance of exploiting keyword dynamics.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abilhoa, W.D., de Castro, L.N.: A keyword extraction method from twitter messages represented as graphs. Appl. Math. Comput. 240, 308–325 (2014). https://doi.org/10.1016/j.amc.2014.04.090

    Article  Google Scholar 

  2. Barrios, F., López, F., Argerich, L., Wachenchauzer, R.: Variations of the similarity function of textrank for automated summarization. CoRR abs/1602.03606 (2016)

    Google Scholar 

  3. Blei, D.M., Lafferty, J.D.: Dynamic topic models. In: Proceedings of the 23rd International Conference on Machine Learning, ICML 2006, pp. 113–120. ACM, New York (2006). https://doi.org/10.1145/1143844.1143859

  4. Campos, R., Mangaravite, V., Pasquali, A., Jorge, A.M., Nunes, C., Jatowt, A.: A text feature based automatic keyword extraction method for single documents. In: Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A. (eds.) ECIR 2018. LNCS, vol. 10772, pp. 684–691. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-76941-7_63

    Chapter  Google Scholar 

  5. Chen, S.F., Goodman, J.: An empirical study of smoothing techniques for language modeling. In: Proceedings of the 34th Annual Meeting on Association for Computational Linguistics, pp. 310–318. Association for Computational Linguistics (1996)

    Google Scholar 

  6. Graus, D., Tsagkias, M., Weerkamp, W., Meij, E., de Rijke, M.: Dynamic collective entity representations for entity ranking. In: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, WSDM 2016, pp. 595–604. ACM, New York (2016). https://doi.org/10.1145/2835776.2835819

  7. Litvak, M., Last, M.: Graph-based keyword extraction for single-document summarization. In: Proceedings of the Workshop on Multi-source Multilingual Information Extraction and Summarization, MMIES 2008, pp. 17–24. Association for Computational Linguistics, Stroudsburg (2008). http://dl.acm.org/citation.cfm?id=1613172.1613178

  8. Liu, F., Pennell, D., Liu, F., Liu, Y.: Unsupervised approaches for automatic keyword extraction using meeting transcripts. In: Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, NAACL 2009, pp. 620–628. Association for Computational Linguistics, Stroudsburg (2009). http://dl.acm.org/citation.cfm?id=1620754.1620845

  9. Liu, Z., Huang, W., Zheng, Y., Sun, M.: Automatic keyphrase extraction via topic decomposition. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, EMNLP 2010, pp. 366–376. Association for Computational Linguistics, Stroudsburg (2010). http://dl.acm.org/citation.cfm?id=1870658.1870694

  10. Marujo, L., et al.: Automatic keyword extraction on twitter. In: 53rd Annual Meeting of the Association for Computational Linguistics. ACL, July 2015

    Google Scholar 

  11. Mihalcea, R., Tarau, P.: TextRank: bringing order into texts. In: Proceedings of EMNLP-04 and the 2004 Conference on Empirical Methods in Natural Language Processing, July 2004

    Google Scholar 

  12. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Burges, C.J.C., Bottou, L., Welling, M., Ghahramani, Z., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 26, pp. 3111–3119. Curran Associates Inc. (2013)

    Google Scholar 

  13. Řehůřek, R., Sojka, P.: Software framework for topic modelling with large corpora. In: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, pp. 45–50. ELRA, Valletta, May 2010. http://is.muni.cz/publication/884893/en

  14. Rose, S., Engel, D., Cramer, N., Cowley, W.: Automatic keyword extraction from individual documents. In: Text Mining: Applications and Theory, pp. 1–20 (2010)

    Google Scholar 

  15. Wu, W., Zhang, B., Ostendorf, M.: Automatic generation of personalized annotation tags for twitter users. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, HLT 2010, pp. 689–692. Association for Computational Linguistics, Stroudsburg (2010). http://dl.acm.org/citation.cfm?id=1857999.1858100

  16. Zhao, W.X., et al.: Topical keyphrase extraction from Twitter. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1, HLT 2011, pp. 379–388. Association for Computational Linguistics, Stroudsburg (2011). http://portal.acm.org/citation.cfm?id=2002472.2002521

  17. Zhao, W.X., et al.: Comparing twitter and traditional media using topic models. In: Clough, P., et al. (eds.) ECIR 2011. LNCS, vol. 6611, pp. 338–349. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20161-5_34

    Chapter  Google Scholar 

Download references

Acknowledgements

This work has been partially funded by the CMU Portugal research project GoLocal Ref. CMUP-ERI/TIC/0033/2014, by the H2020 ICT project COGNITUS with the grant agreement No 687605 and by the project NOVA LINCS Ref. UID/CEC/04516/2013.

Author information

Authors and Affiliations

  1. NOVA LINCS, School of Science and Technology, Universidade NOVA de Lisboa, Caparica, Portugal

    David Semedo & João Magalhães

Authors
  1. David Semedo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. João Magalhães
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to David Semedo .

Editor information

Editors and Affiliations

  1. University of Strathclyde, Glasgow, UK

    Leif Azzopardi

  2. Bauhaus Universität Weimar, Weimar, Germany

    Benno Stein

  3. Universität Duisburg-Essen, Duisburg, Germany

    Norbert Fuhr

  4. GESIS - Leibniz Institute for the Social Sciences, Cologne, Germany

    Philipp Mayr

  5. Delft University of Technology, Delft, The Netherlands

    Claudia Hauff

  6. University of Twente, Enschede, The Netherlands

    Djoerd Hiemstra

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Semedo, D., Magalhães, J. (2019). Dynamic-Keyword Extraction from Social Media. In: Azzopardi, L., Stein, B., Fuhr, N., Mayr, P., Hauff, C., Hiemstra, D. (eds) Advances in Information Retrieval. ECIR 2019. Lecture Notes in Computer Science(), vol 11437. Springer, Cham. https://doi.org/10.1007/978-3-030-15712-8_62

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-15712-8_62

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-15711-1

  • Online ISBN: 978-3-030-15712-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation