SEKE 2012 Proceedings - Knowledge Systems Institute

deployed on Android phone and runs smoothly by connecting
to the Server using WebServices.
We are currently working on enhancing the application
in several ways. We will improve the sentiment dictionary
by automatic training: if certain words always lead to accurate/inaccurate
predictions, their score will be respectively
increased/decreased. We also intend to incorporate social
media (e.g. Twitter) in the application. For example, when
a user searches for a companys stock, along with the quotes,
the application can display the latest tweets related to that
company, whereas the sentiments of these tweets can be
used in the prediction process. Finally, we will include more
news sources as input to the sentiment analysis and update
the user interface to show the news related to that company
which led to that prediction. This way, the user will have a
better idea as to what led to that prediction.
References
Figure 5. User portfolio
are stocks that the user wants to monitor before deciding on
buying/selling them. From the History tab, the user may see
the list of the stocks he/she has traded, along with details on
number of stock, total profit/loss, etc.The Ranking tab allows
the user to see their ranking w.r.t. all other users using
this application, thus encouraging them to play more (the
ranking is based on the total earnings in the simulated environment).
Finally, the Markets tab provides real-time data
for the major equity indices like NASDAQ, S&P500, Dow
Jones (current value and change during the day) and the Profile
Management tab allows the user to manage his/her profile.
6 Conclusions and Future Work
In this paper we present the architecture and a prototype
of a mobile application which provides users virtual money
to buy/sell stocks using real-time data. It allows access to
latest financial news, and provides an option to search company
specific news. An important component of our system
is the stock prediction, which uses sentiment analysis and
machine learning to predict the trend and percentage movement
of a companys stock. We also incorporated ranking of
players based on their performance in order to make the application
interesting to the users. The application has been
[1] S. M. A. Nikfarjam,E.Emadzadeh. Text mining approaches
for stock market prediction. In ICCAE’10, 2010.
[2] J. Bollen, H. Mao, and X. Zeng. Twitter mood predicts the
stock market. Journal of Computational Science, 2(1):1–8,
March 2011.
[3] R. Choudhry and K. Garg. A hybrid machine learning
system for stock market forecasting. In Proceedings of
World Academy of Science Engineering and Technology,
volume 29. May 2008.
[4] K. Jangid and P. Paul. Stock market prediction and simulation
tool (ms thesis). Master’s thesis, Computer Engineering
Dept., San Jose State University, USA, May 2011.
[5] F. Luo, J. Wu, and K. Yan. A novel nonlinear combination
model based on support vector machine for stock market
prediction. In WCICA ’10, 2010.
[6] A. Mahajan, L. Dey, and S. K. M. Haque. Mining financial
news for major events and their impacts on the market.
In IEEE/WIC/ACM Intl. Conf. on Web Intelligence (WI’08),
2008.
[7] R. P. Schumaker and H. Chen. Textual analysis of stock market
prediction using breaking financial news: The azfin text
system. ACM Transactions on Information Systems, 27(2),
2009.
[8] A. Senanayake. Automated neural-ware system for stock
market prediction. In IEEE Conference on Cybernetics and
Intelligent Systems, 2004.
[9] V. H. Shah. Machine learning techniques for stock prediction.
In Foundations of Machine Learning. 2007.
[10] X. Tang, C. Yang, and J. Zhou. Stock price forecasting
by combining news mining and time series analysis. In
IEEE/WIC/ACM Intl. Conf. on Web Intelligence (WI’09),
2009.
[11] P. Yoo, M. Kim, and T. Jan. Machine learning techniques
and use of event information for stock market prediction:
A survey and evaluation. In Intl. Conf. on Computational
Intelligence for Modelling Control and Automation and Intl.
Conf. on Intelligent Agents Web Technologies and Internet
Commerce, volume 2. IEEE, 2007.
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