Conceptual Framework for Stock Market Classification Model Using Sentiment Analysis on Twitter Based on Hybrid Naïve Bayes Classifiers

  • Authors

    • Ghaith Abdulsattar A. Jabbar Alkubaisi
    • Siti Sakira Kamaruddin
    • Husniza Husni
    2018-04-06
    https://doi.org/10.14419/ijet.v7i2.14.11156
  • Classification Accuracy, Naïve Bayes Classifiers, Sentiment Analysis, Stock Market Classification Model, Twitter

  • Abstract

    Sentiment analysis has gained a lot of importance in last decade especially on the availability of data from Twitter that has created more interest for research in this field. Nevertheless, stock market classification models still suffer less accuracy and this has affected negatively the stock market indicators. In this paper, a new framework related to sentiment analysis from Twitter posts is proposed. The proposed framework represents an improved design of classification model that works to improve the classification accuracy to support decision makers in the domain of stock market exchange. This model starts with data collection part and in second phase filtration is done on data to get only the relevant data. The most important phase is the labelling part in which polarity of data is determined and negative, positive or neutral values are assigned to statements of people. The fourth part is the classification phase in which suitable patterns of stock market will be identified by hybridizing NBCs. The last phase is performance and evaluation. This study proposes to a Hybrid Naïve Bayes Classifiers (HNBCs) as a machine learning method for stock market classification, hence represents a useful study for investors, companies and researchers and will help them to formulate their policies according to sentiments of people.

     

     

  • References

    1. [1] Abdelwahab, O., Bahgat, M., Lowrance, C. J., & Elmaghraby, A. (2015, 7-10 Dec. 2015). Effect of training set size on SVM and Naive Bayes for Twitter sentiment analysis. Paper presented at the 2015 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).

      [2] Aggarwal, C. C., & Zhai, C. (2012). Mining text data: Springer Science & Business Media.

      [3] Ahuja, R., Rastogi, H., Choudhuri, A., & Garg, B. (2015). Stock market forecast using sentiment analysis. Paper presented at the Computing for Sustainable Global Development (INDIACom), 2015 2nd International Conference on.

      [4] Al-Ayyoub, M., Essa, S. B., & Alsmadi, I. (2015). Lexicon-based sentiment analysis of arabic tweets. International Journal of Social Network Mining, 2(2), 101-114.

      [5] Alkubaisi, G. A. A., Kamaruddin, S. S., & Husni, H. (2017). A Systematic Review on the Relationship Between Stock Market Prediction Model Using Sentiment Analysis on Twitter Based on Machine Learning Method and Features Selection. Journal of Theoretical and Applied Information Technology, 95(24), 6924-6933.

      [6] Alkubaisi, G. A. A., Kamaruddin, S. S., & Husni, H. (2018). Stock Market Classification Model Using Sentiment Analysis on Twitter Based on Hybrid Naive Bayes Classifiers. Computer and Information Science, 11(1), 52.

      [7] Alm, E. C. O. (2008). Affect in text and speech: ProQuest.

      [8] Aramaki, E., Maskawa, S., & Morita, M. (2011). Twitter catches the flu: detecting influenza epidemics using Twitter. Paper presented at the Proceedings of the conference on empirical methods in natural language processing.

      [9] Arvanitis, K., & Bassiliades, N. (2017). Real-Time Investors’ Sentiment Analysis from Newspaper Articles Advances in Combining Intelligent Methods (pp. 1-23): Springer.

      [10] Atefeh, F., & Khreich, W. (2015). A survey of techniques for event detection in Twitter. Computational Intelligence, 31(1), 132-164.

      [11] Attigeri, G. V., MM, M. P., Pai, R. M., & Nayak, A. (2015). Stock market prediction: A big data approach. Paper presented at the TENCON 2015-2015 IEEE Region 10 Conference.

      [12] Bhattu, N., & Somayajulu, D. (2012). Semi-supervised Learning of Naive Bayes Classifier with feature constraints. Paper presented at the 24th International Conference on Computational Linguistics.

      [13] Bollen, J., & Mao, H. (2011). Twitter Mood as a Stock Market Predictor. Computer, 44(10), 91-94. doi: 10.1109/MC.2011.323

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

      [15] Cakra, Y. E., & Trisedya, B. D. (2015). Stock price prediction using linear regression based on sentiment analysis. Paper presented at the 2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS).

      [16] Chandra, S., Khan, L., & Muhaya, F. B. (2011). Estimating Twitter user location using social interactions--a content based approach. Paper presented at the Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), 2011 IEEE Third International Conference on.

      [17] Di Nunzio, G. M., & Sordoni, A. (2012). A visual tool for bayesian data analysis: the impact of smoothing on naive bayes text classifiers. Paper presented at the Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval.

      [18] Fiarni, C., Maharani, H., & Pratama, R. (2016, 25-27 May 2016). Sentiment analysis system for Indonesia online retail shop review using hierarchy Naive Bayes technique. Paper presented at the 2016 4th International Conference on Information and Communication Technology (ICoICT).

      [19] Gamallo, P., Garcia, M., & Fernández-Lanza, S. (2013). TASS: A Naive-Bayes strategy for sentiment analysis on Spanish tweets. Paper presented at the Workshop on Sentiment Analysis at SEPLN (TASS2013).

      [20] Go, A., Bhayani, R., & Huang, L. (2009). Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford, 1, 12.

      [21] Hajli, M. N. (2014). The role of social support on relationship quality and social commerce. Technological Forecasting and Social Change, 87, 17-27.

      [22] Hamed, A.-R., Qiu, R., & Li, D. (2015). Analysis of the relationship between Saudi Twitter posts and the Saudi stock market. Paper presented at the 2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS).

      [23] He, Y., & Zhou, D. (2011). Self-training from labeled features for sentiment analysis. Information Processing & Management, 47(4), 606-616.

      [24] Hong, L., Ahmed, A., Gurumurthy, S., Smola, A. J., & Tsioutsiouliklis, K. (2012). Discovering geographical topics in the Twitter stream. Paper presented at the Proceedings of the 21st international conference on World Wide Web.

      [25] Jiang, L., Wang, D., Cai, Z., & Yan, X. (2007). Survey of improving naive Bayes for classification. Paper presented at the International Conference on Advanced Data Mining and Applications.

      [26] Koppel, M., & Schler, J. (2006). The importance of neutral examples for learning sentiment. Computational Intelligence, 22(2), 100-109.

      [27] Kouloumpis, E., Wilson, T., & Moore, J. D. (2011). Twitter sentiment analysis: The good the bad and the omg! ICWSM, 11(538-541), 164.

      [28] Li, R., Lei, K. H., Khadiwala, R., & Chang, K. C.-C. (2012). Tedas: A Twitter-based event detection and analysis system. Paper presented at the 2012 IEEE 28th International Conference on Data Engineering.

      [29] Lin, J., & Ryaboy, D. (2013). Scaling big data mining infrastructure: the Twitter experience. ACM SIGKDD Explorations Newsletter, 14(2), 6-19.

      [30] Makice, K. (2009). Twitter API: Up and running: Learn how to build applications with the Twitter API: " O'Reilly Media, Inc.".

      [31] Makrehchi, M., Shah, S., & Liao, W. (2013). Stock prediction using event-based sentiment analysis. Paper presented at the Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2013 IEEE/WIC/ACM International Joint Conferences on.

      [32] Nguyen, T. T. T., & Armitage, G. (2008). A survey of techniques for internet traffic classification using machine learning. IEEE Communications Surveys & Tutorials, 10(4), 56-76. doi: 10.1109/SURV.2008.080406

      [33] Qasem, M., Thulasiram, R., & Thulasiram, P. (2015). Twitter sentiment classification using machine learning techniques for stock markets. Paper presented at the Advances in Computing, Communications and Informatics (ICACCI), 2015 International Conference on.

      [34] Sathyadevan, S., Sarath, P. R., Athira, U., & Anjana, V. (2014, 26-28 Aug. 2014). Improved document classification through enhanced Naive Bayes algorithm. Paper presented at the Data Science & Engineering (ICDSE), 2014 International Conference on.

      [35] Shimada, K., Inoue, S., Maeda, H., & Endo, T. (2011). Analyzing tourism information on Twitter for a local city. Paper presented at the Software and Network Engineering (SSNE), 2011 First ACIS International Symposium on.

      [36] Song, Z., & Xia, J. C. (2016). Spatial and Temporal Sentiment Analysis of Twitter data. European Handbook of Crowdsourced Geographic Information, 205.

      [37] Tan, S., & Zhang, J. (2008). An empirical study of sentiment analysis for chinese documents. Expert Systems with Applications, 34(4), 2622-2629.

      [38] Wang, H., Can, D., Kazemzadeh, A., Bar, F., & Narayanan, S. (2012). A system for real-time Twitter sentiment analysis of 2012 us presidential election cycle. Paper presented at the Proceedings of the ACL 2012 System Demonstrations.

      [39] Wang, H., Wu, J., Zhang, P., & Zhang, C. (2016). Temporal Feature Selection on Networked Time Series. arXiv preprint arXiv:1612.06856.

      [40] Yamamoto, Y. (2014). Twitter4J-A java library for the Twitter API: sep.

      [41] Yan, D., Zhou, G., Zhao, X., Tian, Y., & Yang, F. (2016). Predicting stock using microblog moods. China Communications, 13(8), 244-257. doi: 10.1109/CC.2016.7563727

      [42] Yang, A., Zhang, J., Pan, L., & Xiang, Y. (2015, 16-18 Nov. 2015). Enhanced Twitter Sentiment Analysis by Using Feature Selection and Combination. Paper presented at the Security and Privacy in Social Networks and Big Data (SocialSec), 2015 International Symposium on.

      [43] Zhang, L. (2013). Sentiment analysis on Twitter with stock price and significant keyword correlation (Doctoral dissertation).

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  • How to Cite

    Abdulsattar A. Jabbar Alkubaisi, G., Sakira Kamaruddin, S., & Husni, H. (2018). Conceptual Framework for Stock Market Classification Model Using Sentiment Analysis on Twitter Based on Hybrid Naïve Bayes Classifiers. International Journal of Engineering & Technology, 7(2.14), 57-61. https://doi.org/10.14419/ijet.v7i2.14.11156

    Received date: 2018-04-06

    Accepted date: 2018-04-06

    Published date: 2018-04-06