Stock Price Prediction Method Using Sentiment Analysis of News 


Vol. 48,  No. 6, pp. 748-750, Jun.  2023
10.7840/kics.2023.48.6.748


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  Abstract

Research using deep learning models to predict stock prices is constantly ongoing. Stock prediction deep learning models typically learn the time-series characteristics and use them to predict future values.However, because stock prices are heavily influenced by external factors, predicting stock prices using time-series prediction models that only learn technical data results in lower accuracy. In this paper, we propose a methodology for predicting stock prices using sentiment representations extracted from both stock price data and economic news text. Instead of using stock price data and sentiment representations as inputs to the model simultaneously, we propose a method of combining the intermediate representation of the stock price data and the sentiment representations.

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[IEEE Style]

D. Kim, M. Yun, Y. Cho, Y. Choi, "Stock Price Prediction Method Using Sentiment Analysis of News," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 6, pp. 748-750, 2023. DOI: 10.7840/kics.2023.48.6.748.

[ACM Style]

Daegyeom Kim, Min-Hyeok Yun, Young-Jin Cho, and Yong-Hoon Choi. 2023. Stock Price Prediction Method Using Sentiment Analysis of News. The Journal of Korean Institute of Communications and Information Sciences, 48, 6, (2023), 748-750. DOI: 10.7840/kics.2023.48.6.748.

[KICS Style]

Daegyeom Kim, Min-Hyeok Yun, Young-Jin Cho, Yong-Hoon Choi, "Stock Price Prediction Method Using Sentiment Analysis of News," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 6, pp. 748-750, 6. 2023. (https://doi.org/10.7840/kics.2023.48.6.748)
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