Stock Recommendations using Bio-Inspired Computations on Social Media


  • Sophia Babu Swamiraj Bishop Heber College
  • Rajkumar Kannan Department of Computer Science, Bishop Heber College (Autonomous), Tiruchirappalli, India.



Stock micro blogging, stock investment, recommendations, user generated content, opinion mining, swarm intelligence.


The tremendous growth of the social networks has paved way for social interactions of investing communities about a company’s stock performance. Investors are able to share their comments on stocks using social media platforms. These interactions are captured and mined to produce advice on investing which helps retail investors to do prospective investments to increase profits. In this paper, we propose a novel stock recommendation methodology using ant colony optimization (ACO). This method extracts sentiments from the investor’s stock reviews and performs the sentiment analysis, which is optimized by the ACO. This method helps to find the correlation between sentiments and stock values, to make future stock predictions and to give stock recommendations to the retail investor.


Author Biography

Sophia Babu Swamiraj, Bishop Heber College

department of computer science,

asst professor


(1) Barber, B. M., & Odean, T. (2001). The internet and the investor. The Journal of Economic Perspectives, 15(1), 41-54.

(2) Zhang, Y., & Swanson, P. E. (2010). Are day traders bias free?—evidence from internet stock message boards. Journal of Economics and Finance, 34(1), 96-112.

(3) Ullrich, C., Borau, K., Luo, H., Tan, X., Shen, L., & Shen, R. (2008, April). Why web 2.0 is good for learning and for research: principles and prototypes. In Proceedings of the 17th international conference on World Wide Web (pp. 705-714).

(4) Tumarkin, R., & Whitelaw, R. F. (2001). News or noise? Internet postings and stock prices. Financial Analysts Journal, 57(3), 41-51.

(5) Rheingold, H. (2007). Smart mobs: The next social revolution. Basic books.

(6) Kolbitsch, J., & Maurer, H. A. (2006). The Transformation of the Web: How Emerging Communities Shape the Information we Consume. J. UCS, 12(2), 187-213.

(7) Blum, C., & Li, X. (2008). Swarm intelligence in optimization. In Swarm Intelligence (pp. 43-85).

(8) Dellarocas, C. (2003) “The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms” Management Science, 49(10):1407-1424.

(9) Wasko, M. & Faraj, S. (2005) “Why Should I Share? Examining Social Capital and Knowledge Contribution in Electronic Networks of Practice” MIS Quarterly, 29(1), 35-57.

(10) Wysocki, P. D. (1998). Cheap talk on the web: The determinants of postings on stock message boards. University of Michigan Business School Working Paper, (98025).

(11) Antweiler, W., & Frank, M. Z. (2004). Is all that talk just noise? The information content of internet stock message boards. The Journal of Finance, 59(3), 1259-1294.

(12) Koski, J. L., Rice, E. M., & Tarhouni, A. (2004). Noise trading and volatility: Evidence from day trading and message boards. Available at SSRN 533943.

(13) Das, S. R., & Chen, M. Y. (2007). Yahoo! for Amazon:

Sentiment extraction from small talk on the web. Management Science, 53(9), 1375-1388.

(14) Das, S., Martinez-Jerez, A. & Tufano, P. (2005) “eInformation: A Clinical Study of Investor Discussion and Sentiment” Financial Management, 34(3), 103-137.

(15) Lakonishok, J., Shleifer, A., & Vishny, R. W. (1994). Contrarian investment, extrapolation, and risk. The journal of finance, 49(5), 1541-1578.

(16) Kahneman, D., & Tversky, A. (1973). On the psychology of prediction. Psychological review, 80(4), 237.

(17) Sabherwal, S., Sarkar, S. K., & Zhang, Y. (2008). Online talk: does it matter?. Managerial Finance, 34(6), 423-436.

(18) Bollen, J., Pepe, A. & Mao, H. (2010a) “Modeling public mood and emotion: Twitter sentiment and socioeconomic phenomena” Proceedings from 19th International World Wide Web Conference Raleigh, North Carolina.

(19) Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1-8.

(20) Asur, S., & Huberman, B. A. (2010, August). Predicting the future with social media. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010, (Vol. 1, pp. 492-499).

(21) Goldstone, R. L., & Janssen, M. A. (2005). Computational models of collective behavior. Trends in cognitive sciences, 9(9), 424-430.

(22) Axelrod, R. M. (1997). The complexity of cooperation: Agent-based models of competition and collaboration. Princeton University Press.

(23) Salganik, M. J., Dodds, P. S., & Watts, D. J. (2006). Experimental study of inequality and unpredictability in an artificial cultural market. Science, 311(5762), 854-856.

(24) Loewenstein, G., & Schkade, D. (1999). Wouldn’t it be nice? Predicting future feelings. Well-being: The foundations of hedonic psychology, 85-105.

(25) Bonds-Raacke, J. M., Fryer, L. S., Nicks, S. D., & Durr, R. T. (2001). Hindsight bias demonstrated in the prediction of a sporting event. The Journal of social psychology, 141(3), 349-352.

(26) Schelling, T. C. (1971). Dynamic models of segregation†. Journal of mathematical sociology, 1(2), 143-186.

(27) Axelrod, R. (1997). The dissemination of culture a model with local convergence and global polarization. Journal of conflict resolution, 41(2), 203-226.

(28) Berger, J. A., & Heath, C. (2005). Idea habitats: How the prevalence of environmental cues influences the success of ideas. Cognitive Science, 29(2), 195-221.

(29) Rosenkopf, L., & Abrahamson, E. (1999). Modeling reputational and informational influences in threshold models of bandwagon innovation diffusion. Computational & Mathematical Organization Theory, 5(4), 361-384.

(30) Sakamoto, Y., Sadlon, E., & Nickerson, J. V. (2008). Bellwethers and the emergence of trends in online communities. In Proceedings of the 30th Annual Conference of the Cognitive Science Society (pp. 1416-1421).

(31) Ye, F., & Chen, C. Y. (2005). Alternative KPSO-clustering algorithm. In 淡江理工學刊, 8(2), 165-174.

(32) Admane, L., Benatchba, K., Koudil, M., Siad, L., & Maziz, S. (2006). AntPart: an algorithm for the unsupervised classification problem using ants. Applied Mathematics and Computation, 180(1), 16-28.

(33) Jensen, R. (2006). Performing feature selection with ACO. In Swarm Intelligence in Data Mining (pp. 45-73).

(34) Galea, M., & Shen, Q. (2006). Simultaneous ant colony optimization algorithms for learning linguistic fuzzy rules. In Swarm intelligence in data mining (pp. 75-99).

(35) Abraham, A., & Ramos, V. (2003, December). Web usage mining using artificial ant colony clustering and linear genetic programming. In The IEEE Congress on Evolutionary Computation, 2003. CEC'03., (Vol. 2, pp. 1384-1391).

(36) Ujjin, S., & Bentley, P. J. (2003, April). Particle swarm optimization recommender system. In Proceedings of the 2003 IEEE Swarm Intelligence Symposium, 2003. SIS'03. (pp.


(37) Palotai, Z., Mandusitz, S., & Lórincz, A. (2006). Computer study of the evolution of ‘news foragers' on the Internet. In Swarm Intelligence in Data Mining (pp. 203-219).

(38) Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999). Swarm intelligence: from natural to artificial systems (No. 1). Oxford university press.

(39) Szuba, T. M. (2001). Computational collective intelligence. John Wiley & Sons, Inc..

(40) Hofstätter, P. R. (1986). Gruppendynamik, Kritik der Massenpsychologie; 3. Auflage, Rowohlt, Reinbeck bei Hamburg.

(41) Smith, J. B. (1994). Collective intelligence in computer-based collaboration. CRC Press.

(42) Kolbitsch, J., & Maurer, H. A. (2006). The Transformation of the Web: How Emerging Communities Shape the Information we Consume. J. UCS, 12(2), 187-213.

(43) Johnson, N., Rasmussen, S., Joslyn, C., Rocha, L., Smith, S., & Kantor, M. (1998). Symbiotic Intelligence: self-organizing knowledge on distributed networks driven by human interaction.

In Proceedings of the 6th International Conference on Artificial

Life (pp. 403-407).

(44) Weiss, S. M., Indurkhya, N., Zhang, T., & Damerau, F. (2010). Text mining: predictive methods for analyzing unstructured information.

(45) Ghahramani, Z. (2004). Unsupervised learning, Bousquet O., Raetsch G., and von Luxburg U.(Eds.), Advanced Lectures on Machine Learning, LNAI3176.

(46) Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Reinforcement learning: A survey. Journal of artificial intelligence research, 4, 237-285.

(47) Lee, C. H., Yang, H. C., Chen, T. C., & Ma, S. M. (2006, August). A comparative study on supervised and unsupervised learning approaches for multilingual text categorization. In First International Conference on Innovative Computing, Information and Control-Volume I (ICICIC'06) (Vol. 2, pp. 511-514).

(48) Pang, B., Lee, L., & Vaithyanathan, S. (2002, July). Thumbs up?: sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10 (pp. 79-86).

(49) Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.

(50) Martens, D., De Backer, M., Haesen, R., Baesens, B., &

Holvoet, T. (2006). Ants constructing rule-based classifiers. In Swarm Intelligence in Data Mining (pp. 21-43).

(51) Parpinelli, R. S., Lopes, H. S., & Freitas, A. A. (2002). Data mining with an ant colony optimization algorithm. IEEE transactions on evolutionary computation, 6(4), 321-332.

(52) Stützle, T., & Hoos, H. H. (1996). Improving the Ant System: A detailed report on the MAX–MIN Ant System. FG Intellektik, FB Informatik, TU Darmstadt, Germany, Tech. Rep. AIDA–96–12.

(53) Kaiser, C., Krockel, J., & Bodendorf, F. (2010, January). Swarm intelligence for analyzing opinions in online communities. In 43rd Hawaii International Conference on System Sciences (HICSS), 2010, (pp. 1-9).

(54) Huberman, G. (2001). Familiarity breeds investment. Review of financial Studies, 14(3), 659-680.

(55) Oh, C., & Sheng, O. (2011). Investigating predictive power of stock micro blog sentiment in forecasting future stock price directional movement, In Proceedings of ICIS 2011. 17.




How to Cite

Swamiraj, S. B., & Kannan, R. (2017). Stock Recommendations using Bio-Inspired Computations on Social Media. Transactions on Engineering and Computing Sciences, 5(1), 26.