Skip to main content
SpringerLink
Log in

Sentiment analysis using rule-based and case-based reasoning

  • Published:
Journal of Intelligent Information Systems Aims and scope Submit manuscript

Abstract

Sentiment analysis becomes increasingly popular with the rapid growth of various reviews, survey responses, tweets or posts available from social media like Facebook or Twitter. Sentiment analysis can be turned into the question of whether a piece of text is expressing positive, negative or neutral sentiment towards the discussed topic and can be thus understood as a knowledge-based classification problem. A variety of knowledge-based techniques can be used to solve this problem. The paper focuses on two complementary approaches that originate in the area of AI (artificial intelligence), rule-based reasoning and case-based reasoning. We describe basic principles of both approaches, their strengths and limitations and, based on a review of literature, show how these approaches can be used for sentiment analysis.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Aamodt, A., & Plaza, E. (1994). Case-based reasoning: foundational issues, methodological variations and system approaches. AI Communications, 7(1), 39–59.

    Article  Google Scholar 

  • Aggarwal, C., & Zhai, C. (Eds.). (2012). Mining text data. Berlin: Springer.

    Google Scholar 

  • Ahmed, S., & Danti, A. (2016). Effective sentimental analysis and opinion mining of web reviews using rule based classifiers. In Behera, H.S., & Mohapatra, D.P. (Eds.) Computational intelligence in data mining (pp. 171–197). India: Springer.

  • Bauer, E., & Kohavi, R. (1999). An empirical comparison of voting classification algorithms: bagging, boosting, and variants. Machine Learning, 36(1/2), 105–139.

    Article  Google Scholar 

  • Berka, P. (2011). NEST: a compositional approach to rule-based and case-based reasoning. Advances in Artificial Intelligence.

  • Berka, P., & Ivánek, J. (1994). Automated knowledge acquisition for PROSPECTOR-like expert systems. In Bergadano, J., & deRaedt, L. (Eds.) Proceedings of European conf. on machine learning ECML’94. Springer LNAI 784, Springer (pp. 339–342).

  • Cabrera, M., & Edye, E. (2010). Integration of rule based expert systems and case based reasoning in an acute bacterial meningitis clinical decision support system. International Journal of Computer Science and Information Security, 7(2), 112–118.

    Google Scholar 

  • Cambria, E., Das, D., Bandyopadhyay, S., Feraco, A. (Eds.). (2017). A practical guide to sentiment analysis. Berlin: Springer.

    Google Scholar 

  • Cambria, E., & Hussain, A. (2015). Sentic computing. A Common-Sense-Based Framework for Concept-Level Sentiment Analysis Springer.

  • Chikersal, P., Poria, S., Cambria, E. (2015). SeNTU: sentiment analysis of tweets by combining a rule-based classifier with supervised learning. In Proceedings of the 9th Int. workshop on semantic evaluation, Denver (pp. 647–651).

  • Clark, P., & Niblett, T. (1989). The CN2 induction algorithm. Machine Learning, 3, 261–283.

    Google Scholar 

  • Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv:1702.08608.

  • Ekman, P. (1999). Basic emotions. In Dalgleish, T., & Power, M.J. (Eds.) Handbook of cognition and emotion (pp. 45–60). New York: Wiley.

  • Esuli, A., & Sebastiani, F. (2006). Sentiwordnet: a publicly available lexical resource for opinion mining. In Proceedings fifth international conference on language resources and evaluation LREC, (Vol. 6 pp. 417–422).

  • Feigenbaum, E.A. (1979). Themes and case studies of knowledge engineering. In Michie, D. (Ed.) Expert systems in the micro electronic age: Edinburgh University Press.

  • Feldman, R. (2013). Techniques and applications for sentiment analysis. Communications of the ACM, 56(4), 82–89.

    Article  Google Scholar 

  • Giarratano, J., & Riley, G. (1993). Expert systems. Principles and programming. Boston: PWS Publishing Company.

    Google Scholar 

  • Jurafsky, D., & Martin, J.H. (2008). Speech and language processing. Englewood Cliffs: Prentice Hall.

    Google Scholar 

  • Lee, G.H. (2008). Rule-based and case-based reasoning approach for internal audit of bank. Knowledge-Based Systems, 21, 140–147.

    Article  Google Scholar 

  • Liu, B. (2012). Sentiment analysis and opinion mining. Morgan & Claypool Publishers.

  • Liu, Q., Gao, Z., Liu, B., Zhang, Y. (2015). Automated rule selection for aspect extraction in opinion mining. In Proceedings of the 24th international joint conference on artificial intelligence (IJCAI 2015) (pp. 1291–1297).

  • Michalski, R.S. (1969). On the quasi-minimal solution of the general covering problem. In Proceedings 5th Int. symposium on information processing FCIP’69, Bled, Yugoslavia (pp. 125–128).

  • Ohana, B., Delany, S.J., Tierney, B.A. (2012). Case-based approach to cross domain sentiment classification. In Agudo, B.D., & Watson, I. (Eds.) Case-based reasoning research and Development ICCBR 2012. Berlin: Springer.

  • Otte, C. (2013). Safe and interpretable machine learning, a methodological review. In Moewes, C. (Ed.) Computational intelligence in intelligent data analysis. SCI 445 (pp. 111–122). Berlin: Springer.

  • Plutchik, R. (2002). Emotions and life: perspectives from psychology, Biology, and Evolution. Amer Psychological Assn, USA.

  • Poria, S., Cambria, E., Howard, N., Huang, G.-B., Hussain, A. (2016). Fusing audio, visual and textual clues for sentiment analysis from multimodal content. Neurocomputing, 174, 50–59.

    Article  Google Scholar 

  • Poria, S., Cambria, E., Ku, L.W., Gui, C., Gelbukh, A. (2014). Rule-based approach to aspect extraction from product reviews. In Proceedings of the second workshop on natural language processing for social media, Dublin (pp. 28–37).

  • Prabowo, R., & Thelwall, M. (2013). Sentiment analysis: a combined approach. Journal of Infometrics, 143–157.

  • Qiu, G., He, X., Zhang, F., Shi, Y., Bu, J., Chen, C. (2010). DASA dissatisfaction-oriented advertising based on sentiment analysis. Expert Systems with Applications, 37, 6182–6191.

    Article  Google Scholar 

  • Rashid, A., Asif, S., Butt, N.A., Ashraf, I. (2013). Feature level opinion mining of educational student feedback data using sequential pattern mining and association rule mining. Int. J. of Computer Applications, 81(10), 31–38.

    Article  Google Scholar 

  • Ravi, K., & Ravi, V. (2015). A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowledge-Based Systems, 89, 14–46.

    Article  Google Scholar 

  • Reckman, H., Baird, C., Crawford, J., Crowell, R., Micciulla, L., Sethi, S., Veress, F. (2013). Rule-based detection of sentiment phrases using SAS sentiment analysis. In Proceedings second joint conference on lexical and computational semantics, Atlanta (pp. 513–519).

  • Romanyshin, M. (2013). Rule-based sentiment analysis of ukrainian reviews. Int. J. of Artificial Intelligence & Applications, 4(4), 103–111.

    Article  Google Scholar 

  • Schank, R. (1982). Dynamic memory a theory of learning in computers and people. New York: Cambridge University Press.

    Google Scholar 

  • Srivastava, A.N., & Sahami, M. (2009). Text mining: classification, clustering and applications. Chapman & Hall/CRC Press.

  • Stone, P.J. (1966). The general inquirer. a computer approach to content analysis. Cambridge: MIT Press.

    Google Scholar 

  • Tausczik, Y.R., & Pennebaker, J.W. (2010). The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology, 29(1), 24–54.

    Article  Google Scholar 

  • Wang, G., Sun, J., Ma, J., Xu, K., Gu, J. (2014). Sentiment classification: the contribution of ensemble learning. Decision Support Systems, 57, 77–93.

    Article  Google Scholar 

  • Xi, M., Wu, H., Niu, Z. (2012). A quick emergency response model for microblog public opinion crisis based on text sentiment intensity. Journal of Software, 7(6), 1413–1420.

    Google Scholar 

  • Yang, M., & Shen, Q. (2008). Reinforcing fuzzy rule-based diagnosis of turbomachines with case-based reasoning. Int. J. of Knowledge-Based and Intelligent Engineering Systems, 12(2), 173–181.

    Article  Google Scholar 

  • Zhou, F., Jiao, R.J., Linsey, J.S. (2015). Latent customer needs elicitation by use case analogical reasoning from sentiment analysis of online product reviews. Journal of Mechanical Design, 137.

Download references

Acknowledgements

The paper was processed with the contribution of long term institutional support of research activities by Faculty of Informatics and Statistics, University of Economics, Prague.

Author information

Authors and Affiliations

  1. University of Economics, W. Churchill Sq. 4, Prague, CZ, 130 67, Czech Republic

    Petr Berka

Authors
  1. Petr Berka
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Petr Berka.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Berka, P. Sentiment analysis using rule-based and case-based reasoning. J Intell Inf Syst 55, 51–66 (2020). https://doi.org/10.1007/s10844-019-00591-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10844-019-00591-8

Keywords

Search

Navigation