Semantic Enhanced Social Media Sentiments for Stock Market Prediction
Traditional document representation for classification
follows Bag of Words (BoW) approach to represent the term weights.
The conventional method uses the Vector Space Model (VSM) to
exploit the statistical information of terms in the documents and they
fail to address the semantic information as well as order of the terms
present in the documents. Although, the phrase based approach
follows the order of the terms present in the documents rather than
semantics behind the word. Therefore, a semantic concept based
approach is used in this paper for enhancing the semantics by
incorporating the ontology information. In this paper a novel method
is proposed to forecast the intraday stock market price directional
movement based on the sentiments from Twitter and money control
news articles. The stock market forecasting is a very difficult and
highly complicated task because it is affected by many factors such
as economic conditions, political events and investor’s sentiment etc.
The stock market series are generally dynamic, nonparametric, noisy
and chaotic by nature. The sentiment analysis along with wisdom of
crowds can automatically compute the collective intelligence of
future performance in many areas like stock market, box office sales
and election outcomes. The proposed method utilizes collective
sentiments for stock market to predict the stock price directional
movements. The collective sentiments in the above social media have
powerful prediction on the stock price directional movements as
up/down by using Granger Causality test.
[1] Shom Prasad Das Sudarsan Padhy, “Support Vector Machines for
Prediction of Futures Prices in Indian Stock Market”, International
Journal of Computer Applications, vol. 41 no. 3, 2012.
[2] Tim Loughran and Bill McDonald, “When is a liability not a liability?
textual analysis, dictionaries, and 10-ks”, The Journal of Finance,
vol.66, no. 1, pp.35–65, 2011.
[3] Johan Bollen, Huina Mao, and Xiao-Jun Zeng, “Twitter mood predicts
the stock market”, IEEEComputer, vol. 44 no.10,pp. 91–94, 2011.
[4] Ling-Chun Hung, “The Presidential Election and the Stock Market in
Taiwan”. Journal of Business and Policy Research, 2011.
[5] Ritanjali Majhi, G., Panda, G., “Prediction of S&P 500 and DJIA Stock
Indices using Particle Swarm Optimization Technique”, Proceedings of
IEEE Congress on Evolutionary Computation (CEC 2008), 2008.
[6] Khashei, M., Bijari, M., “A novel hybridization of artificial neural
networks and ARIMA models for time series forecasting”, Applied Soft
Computing, vol.11, no.2, pp. 2664–2675,2011.
[7] Chun, C., Qindhua, M., Shuqiang, L., “Research on Support Vector
Regression in the Stock Market Forecasting”, Springer – Advances in
Intelligent and soft Computing , vol. 148, pp. 607-612, 2012.
[8] Yung – Keun Kwon and Byung-Ro Moon, “A Hybrid Neruogentic
Approach for Stock Forecasting”, IEEE Transactions on Neural
Networks, vol.18, no.3, pp. 851-864, 2007.
[9] Kara, Y., Boyacioglu, M.A., Baykan, O.K, “Predicting Direction of
Stock Price Movement Using Artificial Neural Networks and Support
Vector Machines: The Sample of the Istanbul Stock Exchange”, Expert
Systems with Applications, vol.38, no.5, pp. 5311 -5319, 2011.
[10] Jeffrey Breen. “Mining twitter for airline consumer sentiment”, 2014.
(Online; accessed 20-Dec -2014).
[11] Jeff Gentry. Twitter client for r, 2014. (Online; accessed 20-Dec-2014).
[12] Huang, C.F., 2012, “A Hybrid Stock Selection Model Using Genetic
Algorithm and Support Vector Machines”., Applied Soft Computing,
12(2), pp. 807-818.
[13] Nuno Oliveira, Paulo Cortez, Nelson Areal, “Some Experiments on
Modeling Stock Market Behavior Using Investor Sentiment Analysis
and Posting Volume from Twitter”, Proceedings of WIMS’13, 2013.
[14] Bollen, J., Mao, H., Zeng, X,“Twitter mood predicts the stock markets”.,
Journal of Computational Science, vol.2, no.1, pp. 1-8, 2011.
[15] Xiangyu Tang, Chunyu Yang, Jie Zhou, “Stock Price Forecasting
Combining News Mining and Time Series Analysis”, Proceedings of
IEEE/WIC/ACM International Conference on Web Intelligence and
Intelligent Agent Technology- Workshops, 2009.
[16] Binoy B. Nair, V.P. Mohandas, N.R. Sakthivel, “A Genetic Algorithm
Optimized Decision Tree SVM based Stock Market Trend Prediction
System”, International Journal on Compuer Science and Engineering
vol.2, no.9, pp. 2981-2988, 2010.
[17] Nirmala Devi, K., Murali Bhaskaran, V., 2012, “A Semantic Enhanced
Approach for Online Hotspot Forums Detection”, Proceedings of IEEE
Conference - ICRTIT 2012,pp. 497-501, 2012.
[18] Vapnik, V, “The Nature of Statistical Learning Theory”, Springer-
Verlag, pp. 863-884, 2000.
[19] Clerc, M., Kennedy, J, “The particle swarm-explosion, stability, and
convergence in a multidimensional complex space”, IEEE Transactions
on Evolutionary Computation, vol.6, no.1, pp. 58-7, 2002.
[20] X. Zhang, H. Fuehres, and P. A. Gloor, “Predicting stock market
indicators through twitter i hope it is not as bad as I fear,” Anxiety, pp.
1–8, 2009.
[21] Hu M. and Liu B, “Mining and summarizing customer review”,
Proceedings of ACM Transactions on Knowledge and Data
Engineering, pp.168-177, 2004.
[22] C. W. Granger, “Investigating causal relations by econometric models
and cross-spectral methods”, Econometrica: Journal of the Econometric
Society, pp. 424–438, 1969.
[23] http://finance.yahoo.com, http://www.moneycontrol.com
[1] Shom Prasad Das Sudarsan Padhy, “Support Vector Machines for
Prediction of Futures Prices in Indian Stock Market”, International
Journal of Computer Applications, vol. 41 no. 3, 2012.
[2] Tim Loughran and Bill McDonald, “When is a liability not a liability?
textual analysis, dictionaries, and 10-ks”, The Journal of Finance,
vol.66, no. 1, pp.35–65, 2011.
[3] Johan Bollen, Huina Mao, and Xiao-Jun Zeng, “Twitter mood predicts
the stock market”, IEEEComputer, vol. 44 no.10,pp. 91–94, 2011.
[4] Ling-Chun Hung, “The Presidential Election and the Stock Market in
Taiwan”. Journal of Business and Policy Research, 2011.
[5] Ritanjali Majhi, G., Panda, G., “Prediction of S&P 500 and DJIA Stock
Indices using Particle Swarm Optimization Technique”, Proceedings of
IEEE Congress on Evolutionary Computation (CEC 2008), 2008.
[6] Khashei, M., Bijari, M., “A novel hybridization of artificial neural
networks and ARIMA models for time series forecasting”, Applied Soft
Computing, vol.11, no.2, pp. 2664–2675,2011.
[7] Chun, C., Qindhua, M., Shuqiang, L., “Research on Support Vector
Regression in the Stock Market Forecasting”, Springer – Advances in
Intelligent and soft Computing , vol. 148, pp. 607-612, 2012.
[8] Yung – Keun Kwon and Byung-Ro Moon, “A Hybrid Neruogentic
Approach for Stock Forecasting”, IEEE Transactions on Neural
Networks, vol.18, no.3, pp. 851-864, 2007.
[9] Kara, Y., Boyacioglu, M.A., Baykan, O.K, “Predicting Direction of
Stock Price Movement Using Artificial Neural Networks and Support
Vector Machines: The Sample of the Istanbul Stock Exchange”, Expert
Systems with Applications, vol.38, no.5, pp. 5311 -5319, 2011.
[10] Jeffrey Breen. “Mining twitter for airline consumer sentiment”, 2014.
(Online; accessed 20-Dec -2014).
[11] Jeff Gentry. Twitter client for r, 2014. (Online; accessed 20-Dec-2014).
[12] Huang, C.F., 2012, “A Hybrid Stock Selection Model Using Genetic
Algorithm and Support Vector Machines”., Applied Soft Computing,
12(2), pp. 807-818.
[13] Nuno Oliveira, Paulo Cortez, Nelson Areal, “Some Experiments on
Modeling Stock Market Behavior Using Investor Sentiment Analysis
and Posting Volume from Twitter”, Proceedings of WIMS’13, 2013.
[14] Bollen, J., Mao, H., Zeng, X,“Twitter mood predicts the stock markets”.,
Journal of Computational Science, vol.2, no.1, pp. 1-8, 2011.
[15] Xiangyu Tang, Chunyu Yang, Jie Zhou, “Stock Price Forecasting
Combining News Mining and Time Series Analysis”, Proceedings of
IEEE/WIC/ACM International Conference on Web Intelligence and
Intelligent Agent Technology- Workshops, 2009.
[16] Binoy B. Nair, V.P. Mohandas, N.R. Sakthivel, “A Genetic Algorithm
Optimized Decision Tree SVM based Stock Market Trend Prediction
System”, International Journal on Compuer Science and Engineering
vol.2, no.9, pp. 2981-2988, 2010.
[17] Nirmala Devi, K., Murali Bhaskaran, V., 2012, “A Semantic Enhanced
Approach for Online Hotspot Forums Detection”, Proceedings of IEEE
Conference - ICRTIT 2012,pp. 497-501, 2012.
[18] Vapnik, V, “The Nature of Statistical Learning Theory”, Springer-
Verlag, pp. 863-884, 2000.
[19] Clerc, M., Kennedy, J, “The particle swarm-explosion, stability, and
convergence in a multidimensional complex space”, IEEE Transactions
on Evolutionary Computation, vol.6, no.1, pp. 58-7, 2002.
[20] X. Zhang, H. Fuehres, and P. A. Gloor, “Predicting stock market
indicators through twitter i hope it is not as bad as I fear,” Anxiety, pp.
1–8, 2009.
[21] Hu M. and Liu B, “Mining and summarizing customer review”,
Proceedings of ACM Transactions on Knowledge and Data
Engineering, pp.168-177, 2004.
[22] C. W. Granger, “Investigating causal relations by econometric models
and cross-spectral methods”, Econometrica: Journal of the Econometric
Society, pp. 424–438, 1969.
[23] http://finance.yahoo.com, http://www.moneycontrol.com
@article{"International Journal of Business, Human and Social Sciences:69661", author = "K. Nirmala Devi and V. Murali Bhaskaran", title = "Semantic Enhanced Social Media Sentiments for Stock Market Prediction", abstract = "Traditional document representation for classification
follows Bag of Words (BoW) approach to represent the term weights.
The conventional method uses the Vector Space Model (VSM) to
exploit the statistical information of terms in the documents and they
fail to address the semantic information as well as order of the terms
present in the documents. Although, the phrase based approach
follows the order of the terms present in the documents rather than
semantics behind the word. Therefore, a semantic concept based
approach is used in this paper for enhancing the semantics by
incorporating the ontology information. In this paper a novel method
is proposed to forecast the intraday stock market price directional
movement based on the sentiments from Twitter and money control
news articles. The stock market forecasting is a very difficult and
highly complicated task because it is affected by many factors such
as economic conditions, political events and investor’s sentiment etc.
The stock market series are generally dynamic, nonparametric, noisy
and chaotic by nature. The sentiment analysis along with wisdom of
crowds can automatically compute the collective intelligence of
future performance in many areas like stock market, box office sales
and election outcomes. The proposed method utilizes collective
sentiments for stock market to predict the stock price directional
movements. The collective sentiments in the above social media have
powerful prediction on the stock price directional movements as
up/down by using Granger Causality test.
", keywords = "Bag of Words, Collective Sentiments, Ontology,
Semantic relations, Sentiments, Social media, Stock Prediction,
Twitter, Vector Space Model and wisdom of crowds.", volume = "9", number = "2", pages = "671-5", }
