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Open AccessArticle

Forecasting Oil Price Using Web-based Sentiment Analysis

1
School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China
2
Center for Energy and Environmental Policy Research & School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China
*
Author to whom correspondence should be addressed.
Energies 2019, 12(22), 4291; https://doi.org/10.3390/en12224291
Received: 18 August 2019 / Revised: 6 November 2019 / Accepted: 7 November 2019 / Published: 11 November 2019
(This article belongs to the Section Energy Economics and Policy)
International oil price forecasting is a complex and important issue in the research area of energy economy. In this paper, a new model based on web-based sentiment analysis is proposed. For the oil market, sentiment analysis is used to extract key information from web texts from the four perspectives of: compound, negative, neutral, and positive sentiment. These are constructed as feature and input into oil price forecasting models with oil price itself. Finally, we analyze the effect in various views and get some interesting discoveries. The results show that the root mean squared error can be reduced by about 0.2 and the error variance by 0.2, which means that the accuracy and stability are thereby improved. Furthermore, we find that different types of sentiments can all improve performance but by similar amounts. Last but not least, text with strong intensity can better support oil price forecasting than weaker text, for which the root mean squared error can be reduced by up to 0.5, and the number of the bad cases is reduced by 20%, indicating that text with strong intensity can correct the original oil price forecast. We believe that our research will play a strong supporting role in future research on using web information for oil price forecasting. View Full-Text
Keywords: forecasting; text mining; sentiment analysis; NLP forecasting; text mining; sentiment analysis; NLP
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MDPI and ACS Style

Zhao, L.-T.; Zeng, G.-R.; Wang, W.-J.; Zhang, Z.-G. Forecasting Oil Price Using Web-based Sentiment Analysis. Energies 2019, 12, 4291. https://doi.org/10.3390/en12224291

AMA Style

Zhao L-T, Zeng G-R, Wang W-J, Zhang Z-G. Forecasting Oil Price Using Web-based Sentiment Analysis. Energies. 2019; 12(22):4291. https://doi.org/10.3390/en12224291

Chicago/Turabian Style

Zhao, Lu-Tao; Zeng, Guan-Rong; Wang, Wen-Jing; Zhang, Zhi-Gang. 2019. "Forecasting Oil Price Using Web-based Sentiment Analysis" Energies 12, no. 22: 4291. https://doi.org/10.3390/en12224291

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