Twitter Information Representation using Resource Description Framework

  • Authors

    • Asaad Sabah Hadi
    • Enass Mahdi Abdulshaheed
    2018-11-27
    https://doi.org/10.14419/ijet.v7i4.16.28062
  • Social Media, Twitter, Tweets, RDF, Jena, Information Extraction.

  • Abstract

    Social media service emerged as essential forums for negotiating and giving comments about news, events happening around the world. Such user-generated information can be important data source for researches.Twitter is a micro-blogging which is growing rapidly in the last years.Due to the fast evolution of twitter, the Researchers study tweets’ content characteristics, so this can help in extraction of information like users’ information, opinions about topics and other useful information from tweets.This work proposed a method to extract useful data from tweet object and represent it in Resource description framework (RDF) data model. The Streaming API used to collect twitter data by filtering the results based on specific keywords. The response obtained as Java Script Object Notation (JSON) file,then while parsing the obtained file thetweet’s text preprocessing and extract useful information from other entities of tweet object, Jena API used to represent extracted data in RDF data model and save it in RDF file to be used in future applications.

     

  • References

    1. [1] Fafalios, F., Iosifidis, V., Ntoutsi, E. and Dietze, S. (2018). TweetsKB: A Public and Large-Scale RDF Corpus of Annotated Tweets. (ESWC’18), L3S Research Center.

      [2] Statista – The portal for statistics, https://www.statista.com/statistics/282087/number-of-monthly-active-twitter-users/, [Accessed 23 Jun. 2018].

      [3] Bouazizi, M. and Ohtsuki, T. (2017). A Pattern-Based Approach for Multi-Class Sentiment Analysis in Twitter. IEEE Access, 5, pp.20617-20639.

      [4] Kamel, N. (2012). Ontology-Based Information Extraction fromTwitter, 17-22.

      [5] Asfari, O., Hannachi, L., Bentayeb, F., &Boussaid, O. (2013). Ontological Topic Modeling to Extract Twitter Users' Topics of Interest, ICITA , 141—146.

      [6] Singh, T. and Kumari, M. (2016). Role of Text Pre-processing in Twitter Sentiment Analysis, Elsevier B.V., Volume 89, P. 549-554.

      [7] Bao Y., Quan C., Wang L., Ren F. (2014) The Role of Pre-processing in Twitter Sentiment Analysis. In: Huang DS., Jo KH., Wang L. (eds) Intelligent Computing Methodologies. ICIC 2014. Lecture Notes in Computer Science, vol 8589. Springer, Cham.

      [8] Goonetilleke, O., Sellis, T., Zhang, X. and Sathe, S. (2014). Twitter analytics. ACM SIGKDD Explorations Newsletter, 16(1), pp.11-20.

      [9] Batrinca, B. and Treleaven, P. (2015). Social media analytics: a survey of techniques, tools and platforms. AI & SOCIETY, 30(1), p. 89-116.

      [10] O'Reilly, T. and Milstein, S. (2012). The Twitter book. 2nd ed. Sebastopol, CA: O'Reilly, p.258.

      [11] Russell, M. (2013). Mining the Social Web Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and more, 2nd ed. O'Reilly Media, p. 448.

      [12] Kumar, S., Morstatter, F. and Liu, H. (2014.). Twitter Data Analytics. 1st ed.Springer Briefs in Computer Science, Springer, p.77

      [13] Vigo, M., Bellahsene, Z., Ienco, D. and Todorov, K. (2015). Twitter Event Detection and Modeling with TEWS. ISWC: International Semantic Web Conference, [online] Hal.archives-ouvertes.fr. Available at: https://hal.archives-ouvertes.fr/hal-01274024/ [Accessed 2 Jul. 2018].

      [14] https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/ intro-to-tweet-json.html/, [Accessed 25 Jun. 2018].

      [15] Heath, T. and Bizer, C. (2011). Linked Data: Evolving the Web into a Global Data Space. 1st ed. Morgan & Claypool, pp.1-136.

      [16] Antoniou, G., Van Harmelen, F., Hoekstra, R., Groth, P. and Antoniou, G. (2012). A semantic web primer. 3rd ed. Cambridge: MIT Press.p.270.

      [17] Patel, G. A., &Madia, N. (2016). A Survey: Ontology Based Information Retrieval For Sentiment Analysis. IJSRSET 2(2), 460-465.

      [18] Allemang, D., &Hendler, J. A. (2012). Semantic Web for the working Ontologist: Effective modeling in RDFS and OWL.2nd ed. Waltham, MA: Morgan Kaufmann/Elsevier.p.384.

      [19] Gimpel, K., Schneider, N., Oconnor, B., Das, D., Mills, D., Eisenstein, J., . . . Smith, N. A. (2010). Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments. doi:10.21236/ada547371.

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  • How to Cite

    Sabah Hadi, A., & Mahdi Abdulshaheed, E. (2018). Twitter Information Representation using Resource Description Framework. International Journal of Engineering & Technology, 7(4.19), 880-883. https://doi.org/10.14419/ijet.v7i4.16.28062

    Received date: 2019-03-01

    Accepted date: 2019-03-01

    Published date: 2018-11-27