A Study on the Influence of Semantics on the Analysis of Micro-blog Tags in the Medical Domain

  • Carlos Vicient
  • Antonio Moreno
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8127)


One current research topic in Knowledge Discovery is the analysis of the information provided by users in Web 2.0 social applications. In particular, some authors have devoted their attention to the analysis of micro-blogging messages in platforms like Twitter. A common shortcoming of most of the works in this field is their focus on a purely syntactical analysis. It can be argued that a proper semantic treatment of social tags should lead to more structured, meaningful and useful results that a mere syntactic-based approach. This work reports the analysis of a case study on medical tweets, in which the results of a semantic clustering process over a set of hashtags is shown to provide much better results than a clustering based on their syntactic co-occurrence.


Semantic similarity tags co-occurrence clustering micro blogging 


  1. 1.
    O’Reilly, T.: What Is Web 2.0? Design Patterns and Business Models for the Next Generation of Software (2005)Google Scholar
  2. 2.
    Berners-Lee, T., Hendler, J.: The Semantic Web - A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities. Scientific American 284, 34–43 (2001)CrossRefGoogle Scholar
  3. 3.
    Etzioni, O., Banko, M., Soderland, S., Weld, D.S.: Open information extraction from the web. Commun. ACM 51, 68–74 (2008)CrossRefGoogle Scholar
  4. 4.
    Fensel, D., Bussler, C., Ding, Y., Kartseva, V., Klein, M., Korotkiy, M., Omelayenko, B., Siebes, R.: Semantic web application areas. In: Proceedings of the 7th International Workshop on Applications of Natural Language to Information Systems, NLDB (2002)Google Scholar
  5. 5.
    Brill, E.: Processing natural language without natural language processing. In: Gelbukh, A. (ed.) CICLing 2003. LNCS, vol. 2588, pp. 360–369. Springer, Heidelberg (2003)Google Scholar
  6. 6.
    Holzinger, A., Kickmeier-Rust, M.D., Ebner, M.: Interactive technology for enhancing distributed learning: a study on weblogs. In: Proceedings of the 23rd British HCI Group Annual Conference on People and Computers: Celebrating People and Technology, pp. 309–312. British Computer Society, Swinton (2009)Google Scholar
  7. 7.
    Holzinger, A.: On Knowledge Discovery and Interactive Intelligent Visualization of Biomedical Data - Challenges in Human-Computer Interaction & Biomedical Informatics. In: Helfert, M., Francalanci, C., Filipe, J. (eds.) DATA. SciTePress (2012)Google Scholar
  8. 8.
    Hotho, A., Staab, S., Stumme, G.: Wordnet improves Text Document Clustering. In: Proc. of the SIGIR 2003 Semantic Web Workshop, pp. 541–544 (2003)Google Scholar
  9. 9.
    Cattuto, C., Benz, D., Hotho, A., Stumme, G.: Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems. In: Baumeister, J., Atzmüller, M. (eds.) LWA. Department of Computer Science, pp. 18–26. University of Würzburg, Germany (2008)Google Scholar
  10. 10.
    Cattuto, C., Benz, D., Hotho, A., Stumme, G.: Semantic Grounding of Tag Relatedness in Social Bookmarking Systems. In: Sheth, A.P., Staab, S., Dean, M., Paolucci, M., Maynard, D., Finin, T., Thirunarayan, K. (eds.) ISWC 2008. LNCS, vol. 5318, pp. 615–631. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  11. 11.
    Hold, R.: Twitter in numbers. The Telegraph (2013),
  12. 12.
    Abel, F., Gao, Q., Houben, G.-J., Tao, K.: Semantic Enrichment of Twitter Posts for User Profile Construction on the Social Web. Presented at the (2011) Google Scholar
  13. 13.
    Weng, J., Lim, E.-P., Jiang, J., He, Q.: TwitterRank: finding topic-sensitive influential twitterers. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 261–270. ACM, New York (2010)CrossRefGoogle Scholar
  14. 14.
    Bhulai, S., Kampstra, P., Kooiman, L., Koole, G., Deurloo, M., Kok, B.: Trend visualization in Twitter: what’s hot and what’s not? In: Data Analytics 2012, The First International Conference on Data Analytics, pp. 43–48. IARIA, Barcelona (2012)Google Scholar
  15. 15.
    Pöschko, J.: Exploring Twitter Hashtags. The Computing Research Repository, CoRR (2011)Google Scholar
  16. 16.
    Kywe, S.M., Hoang, T.-A., Lim, E.-P., Zhu, F.: On recommending hashtags in twitter networks. In: Aberer, K., Flache, A., Jager, W., Liu, L., Tang, J., Guéret, C. (eds.) SocInfo 2012. LNCS, vol. 7710, pp. 337–350. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  17. 17.
    Wang, X., Wei, F., Liu, X., Zhou, M., Zhang, M.: Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach. In: CIKM 2011, pp. 1031–1040 (2011)Google Scholar
  18. 18.
    Petz, G., Karpowicz, M., Fürschuß, H., Auinger, A., Stříteský, V., Holzinger, A.: Opinion Mining on the Web 2.0 – Characteristics of User Generated Content and Their Impacts. In: Holzinger, A., Pasi, G. (eds.) HCI-KDD 2013. LNCS, vol. 7947, pp. 35–46. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  19. 19.
    Doan, S., Ohno-Machado, L., Collier, N.: Enhancing Twitter Data Analysis with Simple Semantic Filtering: Example in Tracking Influenza-Like Illnesses. In: Healthcare Informatics, Imaging and Systems Biology (HISB), pp. 62–71. IEEE Computer Society (2012)Google Scholar
  20. 20.
    Russell, M.G., Flora, J., Strohmaier, M., Poschko, J., Rubens, N.: Semantic Analysis of Energy-Related Conversations in Social Media: A Twitter Case Study. In: International Conference of Persuasive Technology (Persuasive 2011), Columbus, OH, USA (2011)Google Scholar
  21. 21.
    Veltri, G.A.: Microblogging and nanotweets: Nanotechnology on Twitter. Public Understanding of Science (2012)Google Scholar
  22. 22.
    Özdikiş, Ö., Şenkul, P., Oguztüzün, H.: Semantic expansion of hashtags for enhanced event detection in Twitter. In: The First International Workshop on Online Social Systems, WOSS (2012)Google Scholar
  23. 23.
    Teufl, P., Kraxberger, S.: Extracting semantic knowledge from twitter. In: Tambouris, E., Macintosh, A., de Bruijn, H. (eds.) ePart 2011. LNCS, vol. 6847, pp. 48–59. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  24. 24.
    Mathiesen, J., Yde, P., Jensen, M.H.: Modular networks of word correlations on Twitter. Sci. Rep. 2 (2012)Google Scholar
  25. 25.
    Fellbaum, C. (ed.): WordNet: An Electronic Lexical Database (Language, Speech, and Communication). MIT Press (1998)Google Scholar
  26. 26.
    Suchanek, F.M., Kasneci, G., Weikum, G.: YAGO: A Large Ontology from Wikipedia and WordNet. Web Semantics 6, 203–217 (2008)CrossRefGoogle Scholar
  27. 27.
    Wu, Z., Palmer, M.: Verbs semantics and lexical selection. In: Proceedings of the 32nd Annual Meeting on Association for Computational Linguistics, pp. 133–138. Association for Computational Linguistics, Stroudsburg (1994)CrossRefGoogle Scholar
  28. 28.
    Martínez, S., Valls, A., Sánchez, D.: Semantically-grounded construction of centroids for datasets with textual attributes. Knowledge-Based Systems 35, 160–172 (2012)CrossRefGoogle Scholar
  29. 29.
    Rada, R., Mili, H., Bicknell, E., Blettner, M.: Development and application of a metric on semantic nets. IEEE Transactions on Systems, Man, and Cybernetics 19, 17–30 (1989)CrossRefGoogle Scholar
  30. 30.
    Budanitsky, A., Hirst, G.: Evaluating WordNet-based Measures of Lexical Semantic Relatedness. Comput. Linguist. 32, 13–47 (2006)zbMATHCrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2013

Authors and Affiliations

  • Carlos Vicient
    • 1
  • Antonio Moreno
    • 1
  1. 1.Department of Computer Science and Mathematics, Intelligent Technologies for Advanced Knowledge Acquisition (ITAKA) research groupUniversitat Rovira i VirgiliTarragonaSpain

Personalised recommendations