Skip to main content
SpringerLink
Log in
Book cover

International Conference on Flexible Query Answering Systems

FQAS 2019: Flexible Query Answering Systems pp 166–175Cite as

Generalized Association Rules for Sentiment Analysis in Twitter

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11529))

Abstract

Association rules have been widely applied in a variety of fields over the last few years, given their potential for descriptive problems. One of the areas where the association rules have been most prominent in recent years is social media mining. In this paper, we propose the use of association rules and a novel generalization of these based on emotions to analyze data from the social network Twitter. With this, it is possible to summarize a great set of tweets in rules based on 8 basic emotions. These rules can be used to categorize the feelings of the social network according to, for example, a specific character.

This is a preview of subscription content, 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   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
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

Learn about institutional subscriptions

References

  1. Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: ACM sigmod record, vol. 22, pp. 207–216. ACM (1993)

    Google Scholar 

  2. Agrawal, R., Srikant, R., et al.: Fast algorithms for mining association rules. In: Proceedings of 20th International Conference on Very Large Databases, VLDB, vol. 1215, pp. 487–499 (1994)

    Google Scholar 

  3. Boztuğ, Y., Reutterer, T.: A combined approach for segment-specific market basket analysis. Eur. J. Oper. Res. 187(1), 294–312 (2008)

    Article  Google Scholar 

  4. Cagliero, L., Fiori, A.: Analyzing twitter user behaviors and topic trends by exploiting dynamic rules. In: Cao, L., Yu, P. (eds.) Behavior Computing. Springer, London (2012). https://doi.org/10.1007/978-1-4471-2969-1_17

    Chapter  Google Scholar 

  5. Cagliero, L., Fiori, A.: Discovering generalized association rules from Twitter. Intell. Data Anal. 17(4), 627–648 (2013)

    Article  Google Scholar 

  6. Delgado, M., Ruiz, M.D., Sanchez, D., Serrano, J.M.: A fuzzy rule mining approach involving absent items. In: Proceedings of the 7th Conference of the European Society for Fuzzy Logic and Technology, pp. 275–282. Atlantis Press (2011)

    Google Scholar 

  7. Erlandsson, F., Bródka, P., Borg, A., Johnson, H.: Finding influential users in social media using association rule learning. Entropy 18(5), 164 (2016)

    Article  Google Scholar 

  8. Hahsler, M., Karpienko, R.: Visualizing association rules in hierarchical groups. J. Bus. Econ. 87(3), 317–335 (2017)

    Google Scholar 

  9. Hai, Z., Chang, K., Kim, J.: Implicit feature identification via co-occurrence association rule mining. In: Gelbukh, A.F. (ed.) CICLing 2011. LNCS, vol. 6608, pp. 393–404. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-19400-9_31

    Chapter  Google Scholar 

  10. Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: ACM sigmod record, vol. 29, pp. 1–12. ACM (2000)

    Google Scholar 

  11. Kwon, K., Jeon, Y., Cho, C., Seo, J., Chung, I.J., Park, H.: Sentiment trend analysis in social web environments. In: 2017 IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 261–268. IEEE (2017)

    Google Scholar 

  12. Michail, A.: Data mining library reuse patterns using generalized association rules. In: Proceedings of the 22nd International Conference on Software Engineering, pp. 167–176. ACM (2000)

    Google Scholar 

  13. Plutchik, R.: The nature of emotions: human emotions have deep evolutionary roots, a fact that may explain their complexity and provide tools for clinical practice. Am. Sci. 89(4), 344–350 (2001)

    Article  Google Scholar 

  14. Ruiz, M.D., Gómez-Romero, J., Molina-Solana, M., Campaña, J.R., Martín-Bautista, M.J.: Meta-association rules for mining interesting associations in multiple datasets. Appl. Soft Comput. 49, 212–223 (2016)

    Article  Google Scholar 

  15. Salas-Zárate, M.P., Medina-Moreira, J., Lagos-Ortiz, K., Luna-Aveiga, H., Rodriguez-Garcia, M.A., Valencia-García, R.: Sentiment analysis on tweets about diabetes: an aspect-level approach. Computational and mathematical methods in medicine 2017 (2017)

    Google Scholar 

  16. Silverstein, C., Brin, S., Motwani, R., Ullman, J.: Scalable techniques for mining causal structures. Data Min. Knowl. Disc. 4(2–3), 163–192 (2000)

    Article  Google Scholar 

  17. Srikant, R., Agrawal, R.: Mining generalized association rules. Future Gener. Comput. Syst. 13(2–3), 161–180 (1997)

    Article  Google Scholar 

  18. Yuan, X., Buckles, B.P., Yuan, Z., Zhang, J.: Mining negative association rules. In: Proceedings of Seventh International Symposium on Computers and Communications, ISCC 2002, pp. 623–628. IEEE (2002)

    Google Scholar 

Download references

Acknowledgment

This research paper is part of the COPKIT project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 786687.

Author information

Authors and Affiliations

  1. Department of Computer Science and A.I., University of Granada, Daniel Saucedo Aranda, s/n, 18014, Granada, Spain

    J. Angel Diaz-Garcia, M. Dolores Ruiz & Maria J. Martin-Bautista

Authors
  1. J. Angel Diaz-Garcia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Dolores Ruiz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maria J. Martin-Bautista
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to J. Angel Diaz-Garcia .

Editor information

Editors and Affiliations

  1. University of Calabria, Rende, Italy

    Alfredo Cuzzocrea

  2. University of Calabria, Rende, Italy

    Sergio Greco

  3. Legind Technologies , Esbjerg , Denmark

    Henrik Legind Larsen

  4. University of Calabria, Rende, Italy

    Domenico Saccà

  5. Roskilde University, Roskilde, Denmark

    Troels Andreasen

  6. Roskilde University, Roskilde, Denmark

    Henning Christiansen

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

Diaz-Garcia, J.A., Ruiz, M.D., Martin-Bautista, M.J. (2019). Generalized Association Rules for Sentiment Analysis in Twitter. In: Cuzzocrea, A., Greco, S., Larsen, H., Saccà, D., Andreasen, T., Christiansen, H. (eds) Flexible Query Answering Systems. FQAS 2019. Lecture Notes in Computer Science(), vol 11529. Springer, Cham. https://doi.org/10.1007/978-3-030-27629-4_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-27629-4_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-27628-7

  • Online ISBN: 978-3-030-27629-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation