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Predicting Stock Performance by Analyzing Emotions of the Public

by Yash Jajoo, Shridhar Kamble
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 177 - Number 1
Year of Publication: 2017
Authors: Yash Jajoo, Shridhar Kamble
10.5120/ijca2017915657

Yash Jajoo, Shridhar Kamble . Predicting Stock Performance by Analyzing Emotions of the Public. International Journal of Computer Applications. 177, 1 ( Nov 2017), 18-20. DOI=10.5120/ijca2017915657

@article{ 10.5120/ijca2017915657,
author = { Yash Jajoo, Shridhar Kamble },
title = { Predicting Stock Performance by Analyzing Emotions of the Public },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2017 },
volume = { 177 },
number = { 1 },
month = { Nov },
year = { 2017 },
issn = { 0975-8887 },
pages = { 18-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume177/number1/28591-2017915657/ },
doi = { 10.5120/ijca2017915657 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:44:42.029206+05:30
%A Yash Jajoo
%A Shridhar Kamble
%T Predicting Stock Performance by Analyzing Emotions of the Public
%J International Journal of Computer Applications
%@ 0975-8887
%V 177
%N 1
%P 18-20
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Prediction of stock markets has been a significant research area. Especially the study of changes in stock prices due to non-quantifiable factors. Here, the concept of fluctuations in the values of stocks due to people’s emotional state is explored. In this approach, sentiment analysis is performed on Twitter data (tweets), the results of which are fed into a prediction algorithm along with stock data from Dow Jones Industrial Average (DJIA). Here, sentiment analysis is based on lexicons as well as heuristics and it determines the tweets’ emotional polarity and classifies them as either positive or negative. Results obtained show 100% accuracy in mapping the tweets’ sentiments to the change in stock prices and the average deviation between predicted and real stock values is 1.77.

References
  1. J. Bollen and H. Mao., “Twitter mood as a stock market predictor,” IEEE Computer, vol. , no. 44(10):91–94
  2. Anurag Nagar, Michael Hahsler, “Using Text and Data Mining Techniques to extract Stock Market Sentiment from Live News Streams,” IPCSIT vol., no. XX (2012) IACSIT Press, Singapore
  3. J. Bean, “R by example: Mining Twitter for consumer attitudes towards airlines,” In Boston Predictive Analytics Meetup Presentation, Feb 2011
  4. Yauheniya Shynkevich, “T.M. McGinnity, Sonya Coleman, Ammar Belatreche, Predicting Stock Price Movements Based on Different Categories of News Articles,” 2015 IEEE Symposium Series on Computational Intelligence
  5. T. Rao and S. Srivastava, "TweetSmart: Hedging in markets through Twitter," 2012 Third International Conference on Emerging Applications of Information Technology, Kolkata, 2012, pp. 193-196. doi: 10.1109/EAIT.2012.6407894.
  6. Clement Levallois, Umigon. Retrieved from http://www.clementlevallois.net/download/umigon.pdf
Index Terms

Computer Science
Information Sciences

Keywords

Prediction algorithm Sentiment analysis Stock prediction.

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