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Predicting Stock Performance by Analyzing Emotions of the Public

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

Yash Jajoo and Shridhar Kamble. Predicting Stock Performance by Analyzing Emotions of the Public. International Journal of Computer Applications 177(1):18-20, November 2017. BibTeX

@article{10.5120/ijca2017915657,
	author = {Yash Jajoo and Shridhar Kamble},
	title = {Predicting Stock Performance by Analyzing Emotions of the Public},
	journal = {International Journal of Computer Applications},
	issue_date = {November 2017},
	volume = {177},
	number = {1},
	month = {Nov},
	year = {2017},
	issn = {0975-8887},
	pages = {18-20},
	numpages = {3},
	url = {http://www.ijcaonline.org/archives/volume177/number1/28591-2017915657},
	doi = {10.5120/ijca2017915657},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, 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

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Keywords

Prediction algorithm, Sentiment analysis, Stock prediction.