Forecasting Stock Market Alternations Using Social Media Sentiment Analysis and Regression Techniques-Reference-Cited by-同舟云学术

Forecasting Stock Market Alternations Using Social Media Sentiment Analysis and Regression Techniques

Published:2023 Issue: Volume: Page:335-346
ISSN:1868-4238
Container-title:IFIP Advances in Information and Communication Technology
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Author:

Saravanos Christina,Kanavos Andreas

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-34171-7_27

Reference33 articles.

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2. Ampomah, E.K., Qin, Z., Nyame, G., Botchey, F.E.: Stock market decision support modeling with tree-based adaboost ensemble machine learning models. Informatica (Slovenia) 44(4) (2020)

3. Baltas, A., Kanavos, A., Tsakalidis, A.K.: An apache spark implementation for sentiment analysis on twitter data. In: 2nd International Workshop on Algorithmic Aspects of Cloud Computing (ALGOCLOUD), vol. 10230, pp. 15–25 (2016)

4. Barrow, D.K., Crone, S.F.: A comparison of adaboost algorithms for time series forecast combination. Int. J. Forecast. 32(4), 1103–1119 (2016)

5. Bing, L., Chan, K.C.C., Ou, C.X.: Public sentiment analysis in twitter data for prediction of a company’s stock price movements. In: 11th IEEE International Conference on e-Business Engineering (ICEBE), pp. 232–239 (2014)

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1. Forecasting Stock Market Alternations Using Social Media Sentiment Analysis and Deep Neural Networks;2023 14th International Conference on Information, Intelligence, Systems & Applications (IISA);2023-07-10