Ant colony based approach to predict stock market movement from mood collected on Twitter

Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013

Volumn , Issue , 2013, Pages 837-845

  Bouktif, Salah ^a   Awad, Mamoun Adel ^a  

^a UNITED ARAB EMIRATES UNIVERSITY   (United Arab Emirates)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMMERCE; FINANCE; FORECASTING;

ANT COLONY OPTIMIZATION ALGORITHMS; BAYESIAN CLASSIFIER; BOOSTING ALGORITHM; INTERPRETABILITY; PREDICTION MODEL; PROBLEM-BASED; PROFILE OF MOOD STATE; STOCK MARKET PREDICTION;

SOCIAL NETWORKING (ONLINE);

EID: 84893293043     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2492517.2500282     Document Type: Conference Paper

References (31)

1. S. Asur and B. A. Huberman. Predicting the future with social media. In International Conference on Web Intelligence and Intelligent Agent Technology, pages 492-499, 2010.
2. J. Bollen, H. Mao, and X. Zeng. Twitter mood predicts the stock market. Journal of Computational Science, 2(1):1-8, 2011.
3. S. Bouktif, F. Ahmed, I. Khalil, and G. Antoniol. A novel composite model approach to improve software quality prediction. Information and Software Technology, 52(12):1298-1311, 2010.
4. J. Chen, H. Huang, S. Tian, and Y. Qu. Feature selection for text classification with naïve bayes. Expert Systems with Applications, 36(3):5432-5435, 2009.
5. A. De Vany and W. D. Walls. Uncertainty in the movie industry: Does star power reduce the terror of the box office? Journal of Cultural Economics, 23(4):285-318, 1999.
6. J. Deneubourg, S. Aron, S. Goss, and J. Pasteels. The self-organizing exploratory pattern of the argentine ant. Journal of insect behavior, 3(2):159-168, 1990.
7. M. Dorigo, M. Birattari, and T. Stutzle. Ant colony optimization. Computational Intelligence Magazine, IEEE, 1(4):28-39, 2006.
8. C. Elkan. Naive bayesian learning. Technical report, Department of Computer Science Harvard University, 1997.
9. N. Fenton and M. Neil. A critique of software defect prediction models. IEEE Transactions on Software Engineering, 25(5):675-689, 1999.
10. N. Fenton and N. Ohlsson. Quantitative analysis of faults and failures in a complex sofware system. IEEE Transactions on Software Engineering, 26(8):797-814, 2000.
11. Y. Freund and R. Schapire. A decision-theoretic generalization of online learning and an application to boosting. Journal of Computer and System Sciences, 55(1):119-139, 1997.
12. M. Galar, A. Fernández, E. Barrenechea, H. Bustince, and F. Herrera. A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 42(4):463-484, 2012.
13. D. Gayo-Avello. I wanted to predict elections with twitter and all i got was this lousy paper a balanced survey on election prediction using twitter data. arXiv preprint arXiv:1204.6441, 2012.
14. J. Ginsberg, M. H. Mohebbi, R. S. Patel, L. Brammer, M. S. Smolinski, and L. Brilliant. Detecting influenza epidemics using search engine query data. Nature, 457(7232):1012-1014, 2008.
15. M. Goebel and L. Gruenwald. A survey of data mining and knowledge discovery software tools. ACM SIGKDD Explorations Newsletter, 1(1):20-33, 1999.
16. G. H. John and P. Langley. Estimating continuous distributions in bayesian classifiers. In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, pages 338-345, 1995.
17. H. Kim, W.-Y. Loh, Y.-S. Shih, and P. Chaudhuri. Visualizable and interpretable regression models with good prediction power. IIE Transactions, 39(6):565-579, 2007.
18. N. Lavrač. Selected techniques for data mining in medicine. Artificial intelligence in medicine, 16(1):3-23, 1999.
19. Y. Liu. Word of mouth for movies: Its dynamics and impact on box office revenue. Journal of marketing, pages 74-89, 2006.
20. H. Lounis and L. Ait-Mehedine. Machine-learning techniques for software product quality assessment. In QSIC, pages 102-109. IEEE Computer Society, 2004.
21. R. Meir, R. El-Yaniv, and S. Ben-David. Localized boosting. In Proceedings of the 13th Annual Conference on Computational Learning Theory, pages 190-199, 2000.
22. C. Merz. Classification and Regression by Combining Models. PhD thesis, university of California Irvine, 1998.
23. R. S. Michalski, J. G. Carbonell, and T. M. Mitchell. Machine learning: An artificial intelligence approach, volume 2. Morgan Kaufmann, 1986.
24. P. Moerland and E. Mayoraz. DynaBoost: Combining boosted hypotheses in a dynamic way. Technical report, IDIAP, Switzerland, 1999.
25. N. Oza and K. Tumer. Classifier ensembles: Select real-world applications. Information Fusion, 9(1):4-20, 2008.
26. R. Ramoni and P. Sebastiani. Robust bayesian classification. Technical report, Knowledge Media Institute, the Open University, 1999.
27. R. Renger. A review of the profile of mood states (poms) in the prediction of athletic success. Journal of Applied Sport Psychology, 5(1):78-84, 1993.
28. M. Trusov, R. E. Bucklin, and K. Pauwels. Effects of word-ofmouth versus traditional marketing: Findings from an internet social networking site. Journal of marketing, 73(5):90-102, 2009.
29. M. van Gerven, R. Jurgelenaite, B. Taal, T. Heskes, and P. Lucas. Predicting carcinoid heart disease with the noisy-threshold classifier. Artificial Intelligence in Medicine, 40(1):45-55, 2007.
30. W. J. Youden. How to evaluate accuracy. Materials Research and Standards, ASTM, 1961.
31. S. Yu and S. Kak. A survey of prediction using social media. arXiv preprint arXiv:1203.1647, 2012.