SENTIMENT ANALYSIS OF STOCK NEWS USING NLTK


EOI: 10.11242/viva-tech.01.05.232

Download Full Text here



Citation

Ms. Hardik Raut, Ms. Rohini Hodge, Ms. Harshal Bhoir, Prof.Ameya purandare , "SENTIMENT ANALYSIS OF STOCK NEWS USING NLTK ", VIVA-IJRI Volume 1, Issue 5, Article 232, pp. 1-6, 2022. Published by Computer Engineering Department, VIVA Institute of Technology, Virar, India.

Abstract

Efficient Market Hypothesis is the popular theory about stock prediction. With its failure much research has been carried in the area of prediction of stocks. This project is about taking non quantifiable data such as financial news articles about a company and predicting its future stock trend with news sentiment classification. Assuming that news articles have impact on stock market, this is an attempt to study relationship between news and stock trend. To show this, we created a NLTK models which depict polarity of news articles being positive or negative. Experiments are conducted to evaluate various aspects of the proposed model. The NLTK model is accurate and this tool is able to determine the emotional values without neutral sections. Comparing these results with the movement of stock market values in the same time periods, we can establish the moment of the change occurred in the stock values with sentiment analysis of economic news headlines. The accuracy of the prediction model is more than 80% and in comparison, with news random labelling with 50% of accuracy; the model has increased the accuracy by 30%.

Keywords

blockchain , Stocks, NTLK

References

  2. Anurag Nagar, Michael Hahsler, Using Text and Data Mining Techniques to extract Stock Market Sentiment from Live News Streams, IPCSIT vol. XX (2012) IACSIT Press, Singapore
  3. W.B. Yu, B.R. Lea, and B. Guruswamy, A Theoretic Framework Integrating Text Mining and Energy Demand Forecasting, International Journal of Electronic Business Management. 2011, 5(3): 211-224
  4. J. Bean, R by example: Mining Twitter for consumer attitudes towards airlines, In Boston Predictive Analytics Meetup Presentation, 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. P. Hofmarcher, S. Theussl, and K. Hornik, Do Media Sentiments Reflect Economic Indices? Chinese Business Review. 2011, 10(7): 487-492
  6. R. Goonatilake and S. Herath, The volatility of the stock market and news, International Research Journal of Finance and Economics, 2007, 11: 53-65.
  7. Spandan Ghose Chowdhury, Soham Routh , Satyajit Chakrabarti, News Analytics and Sentiment Analysis to Predict Stock Price Trends, (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (3) , 2014, 3595-3604
  8. Robert P. Schumaker, Yulei Zhang, Chun-Neng Huang, Sentiment Analysis of Financial News Articles
  9. Gyozo Gidofalvi, Using News Articles to Predict Stock Price Movements, University of California, San Diego La Jolla, CA 92037, 2001
  10. L. Breiman, Random forests. Machine Learning, 45(1):5-32, 2001
  11. Data Mining Lab 7: Introduction to Support Vector Machines (SVMS)
  12. Joachims T., Text Categorization with Support Vector Machines: Learning with Many Relevant Features, European Conference on Machine Learning (ECML), Application of Machine Learning and Data mining in Finance, Chemnitz, Germany, 1998)
  13. Kyoung-jae Kim, Financial time series forecasting using support vector machines, Neurocomputing 55 (2013) 307 - 319
  14. Pegah Falinouss, Stock Trend Prediction using News articles, The Lulea University of Technology, 2007 [15] https://en.wikipedia.org/wiki/Support_vector_machine