Forecasting Oil Price Using Web-based Sentiment Analysis

Lu-Tao Zhao, Guan-Rong Zeng, Wen-Jing Wang and Zhi-Gang Zhang
Additional contact information
Guan-Rong Zeng: School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China
Wen-Jing Wang: School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China
Zhi-Gang Zhang: School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China

Energies, 2019, vol. 12, issue 22, 1-18

Abstract: 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.

Keywords: forecasting; text mining; sentiment analysis; NLP (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
https://www.mdpi.com/1996-1073/12/22/4291/pdf (application/pdf)
https://www.mdpi.com/1996-1073/12/22/4291/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:12:y:2019:i:22:p:4291-:d:285712

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().