Interpretability of computational models for sentiment analysis

Studies in Computational Intelligence

Volumn 639, Issue , 2016, Pages 199-220

  Liu, Han ^a   Cocea, Mihaela ^a   Gegov, Alexander ^a  

^a UNIVERSITY OF PORTSMOUTH   (United Kingdom)

Author keywords

Computational intelligence; Fuzzy computational models; Interpretability analysis; Interpretability evaluation; Machine learning; Rule based networks; Rule based systems; Sentiment prediction

Indexed keywords

EID: 84961662080     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-319-30319-2_9     Document Type: Chapter

References (43)

1. Schlager, J.: Systems engineering: key to modern development. IRE Trans. EM 3(3), 64–66 (1956)
2. Hall, A.D.: A Methodology for Systems Engineering. Van Nostrand Reinhold (1962)
3. Mitchell, T.M.: Machine Learning, McGraw Hill (1997)
4. Liu, H., Gegov, A., Stahl, F.: Categorization and construction of rule based systems. In: 15th International Conference on Engineering Applications of Neural Networks, Sofia, Bulgaria (2014)
5. Ross, T.J.: Fuzzy Logic with Engineering Applications. John Wiley & Sons Ltd, West Sussex (2004)
6. Simpson, S.G.: Mathematical Logic, PA (2013)
7. Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Pearson Education, New Jersey (2006)
8. Higgins, C.M.: Classification and Approximation with Rule Based Networks. Pasadena, California (1993)
9. Uttley, A.M.: The design of conditional probability computers. Inf. Control 2, 1–24 (1959)
10. Kononenko, I.: Bayesain Neual Networks. Biol. Cybern. 61, 361–370 (1989)
11. Rosenblatt, F.: Principles of Neurodynamics: Perceptron and the Theory of Brain Mechanisms. Spartan Books, Washington, DC (1962)
12. Ekeberg, O., Lansner, A.: Automatic generation of internal representations in a probabilistic artificial neural network. In: Proceedings of the First European Conference on Neural Networks (1988)
13. Altrabsheh, N., Cocea, M., Fallahkhair, S.: Sentiment analysis: towards a tool for analysing realtime students feedback. In: 2014 IEEE 26th International Conference on Tools with Artificial Intelligence, Limassol, Cyprus (2014)
14. Liu, B.: Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers (2012)
15. Stahl, F., Jordanov, I.: An overview of use of neural networks for datamining tasks. In: WIREs: Data Mining and Knowledge Discovery, pp. 193–208 (2012)
16. Liu, H., Gegov, A., Stahl, F.: J-measure based hybrid pruning for complexity reduction in classification rules. WSEAS Trans. Syst. 12(9), 433–446 (2013)
17. Liu, H., Gegov, A., Stahl, F.: Unified framework for construction of rule based classification systems. In: Pedrycz, W., Chen, S.M. (eds.) Inforamtion Granularity, Big Data and Computational Intelligence, vol. 8, pp. 209–230, Springer (2015)
18. Quinlan, R.: C4.5: Programs for Machine Learning, Morgan Kaufman (1993)
19. Fürnkranz, J.: Separate-and-conquer rule learning. Artif. Intell. Rev. 13, 3–54 (1999)
20. Cendrowska, J.: PRISM: an algorithm for inducing modular rules. Int. J. Man-Machine Stud. 27, 349–370 (1987)
21. Deng, X.: A Covering-Based Algorithm for Classification: PRISM. Regina (2012)
22. Liu, H., Gegov, A., Cocea, M.: Network Based Rule Representation for Knowledge Discovery and Predictive Modelling. In: IEEEInternationalConference on Fuzzy Systems, Istanbul (2015)
23. Rish, I.: An empirical study of the Naïve Bayes classifier. In: IJCAI 2001Workshop on Empirical Methods in Artificial Intelligence, vol. 3, no. 22, pp. 41–46 (2001)
24. Liu, H., Gegov, A., Cocea, M.: Rule Based Systems for Big Data: A Machine Learning Approach, vol. 13. Springer, Berlin (2016)
25. Quinlan, R.: Discovering rules from large collections of examples: a case study. In: Michie, D. (ed.) Expert Systems in the Micro-Electronic Age, pp. 168–201. Edinburgh, Edinburgh (1979)
26. Lichman, M.: Machine Learning Repository, University of California, School of Information and Computer Science (2013). http://archive.ics.uci.edu/ml
27. Elomaa, T., Kääriäinen, M.: An analysis of reduced error pruning. J. Artif. Intell. Res. 15(1), 163–187 (2001)
28. Altrabsheh, N., Cocea, M., Fallahkhair, S.: Learning sentiment from students’ feedback for real-time interventions in classrooms. In: Adaptive and intelligent systems: Third International Conference, ICAIS 2014, Bournemouth (2014)
29. Kummer, O., Savoy, J.: Feature Selection in Sentiment Analysis. In: CORIA 2012, Bordeaux (2012)
30. Shanno, C.E.: A mathematical theory of communication. Bell Syst. Tech. J. 27(3), 379–423 (1948)
31. Jolliffe, I.T.: Principal Component Analysis. Springer, New York (2002)
32. Yu, H., Yang, J.: A direct LDA algorithm for high-dimensional data—with application to face recognition. Patt. Recogn. 34(10), 2067–2069 (2001)
33. Yates, D.S., Moore, D.S., Starnes, D.S.: The Practice of Statistics, 3rd ed. Freeman (2008)
34. Deming, W.E.: On probability as a basis for action. Am. Stat. 29(4), 146–152 (1975)
35. Kerry, S.M., Bland, J.M.: The intracluster correlation coefficient in cluster randomisation. Br. Med. J. 316(7142), 1455–1460 (1998)
36. Kerber, R.: ChiMerge: discretization of numeric attributes. In: Proceedings of the 10thNational Conference on Artificial Intelligence, California (1992)
37. Medhat, W., Hassan, A., Korashy, H.: Sentiment analysis algorithms and applications: a survey. Ain Shams Eng. J. 5(4), 1093–1113 (2014)
38. Yi, H., Li, W.: Document sentiment classification by exploring description model of topical terms. Comput. Speech Lang. 50, 386–403 (2011)
39. Lewis, D.D., Ringuette, M.: A comparison of two learning algorithms for text categorization. In: Third Annual Symposium on Document Analysis and Information Retrieval, Las Vegas (1994)
40. Chakrabarti, S., Roy, S., Soundalgekar, M.V.: Fast and accurate text classification via multiple linear discriminant projections. Int. J. Very Large Data Bases 12(2), 170–185 (2003)
41. Zhao, C., Wang, S., Li, D.: Fuzzy sentiment membership determining for sentiment classification. In: IEEE International Conference on Data Mining Workshop (ICDMW), Shenzhen (2014)
42. Nadali, S., Murad, M., Kadir, R.: Sentiment classification of customer reviews based on fuzzy logic. In: International Symposium in Information Technology (ITSim), Kuala Lumpur (2010)
43. Colace, F., Santo, M.D., Greco, L.: Aprobabilistic approach to Tweets’ sentiment classification. In: 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, Geneva (2013)