Skip to main content
SpringerLink
Log in

Review and Prospect of Text Analysis Based on Deep Learning and Its Application in Macroeconomic Forecasting

  • Conference paper
  • First Online:
Big Data Analytics for Cyber-Physical System in Smart City (BDCPS 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1303))

  • 83 Accesses

Abstract

Today machine learning is applied in economy, finance and other aspects. Text information has real-time and high values, and is widely used in the emotion analysis and prediction. This article reviews research papers which analyze machine learning and deep learning, and summarizes the application of text analysis in macroeconomic prediction. Finally, it puts forward the development direction of macroeconomic prediction based on deep learning and text analysis, constructs the overall research framework, and proposes development ideas in the future.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 299.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 379.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Conference on Empirical Methods in Natural Language Processing, pp. 10–15, June 2002

    Google Scholar 

  2. Antweiler, W., Frank, M.Z.: Is all that talk just noise? The information content of internet stock message boards. J. Financ. 59(3), 9–12 (2004)

    Article  Google Scholar 

  3. Manela, A., Moreira, A.: News implied volatility and disaster concerns. J. Financ. Econ. 123(1), 19–29 (2017)

    Article  Google Scholar 

  4. Chen, Y., Huang, Z., Li, J., et. al.: Can text-based investor sentiment help understand Chinese stock market? A deep learning method. Working Paper, no. 11, pp. 45–51 (2004)

    Google Scholar 

  5. Sun, J.W., Lv, X.Q., Zhang, L.H.: Sentiment analysis of Chinese microblog based on dictionary and machine learning. Comput. Appl. Softw. 31(7), 177–182 (2014)

    Google Scholar 

  6. Ester, M., Kriegel, H.P., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: International Conference on Knowledge Discovery and Data Mining, pp. 11–21, April 1996

    Google Scholar 

  7. Li, Y.Y., Su, L., Chen, J., et al.: Semi-supervised learning for question classification in CQA. Nat. Comput. 16(4), 567–577 (2016)

    Article  MathSciNet  Google Scholar 

  8. Read, J., Carroll, J.: Weakly supervised techniques for domain-independent sentiment classification. In: Proceedings of the 1st International CIKM Workshop on Topic-sentiment Analysis for Mass Opinion, pp. 21–27, June 2009

    Google Scholar 

  9. Xiao, J., Liu, X.X., Xie, L., et al.: Research on semi supervised integrated model of cost sensitive target customer selection. China Manag. Sci. 26(11), 189–199 (2018)

    Google Scholar 

  10. Titov, I.: Domain adaptation by constraining inter-domain variability of latent feature representation. In: The Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 19–24, June 2011

    Google Scholar 

  11. Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313, 57–86 (2006)

    Article  MathSciNet  Google Scholar 

  12. Kim, S.H., Kim, D.: Investor sentiment from internet message postings and the predictability of stock returns. J. Econ. Behav. Organ. 107, 17–22 (2014)

    Article  Google Scholar 

  13. Elman, J.L.: Finding structure in time. Cogn. Sci. 14(2), 33–36 (1990)

    Article  Google Scholar 

  14. Lai, S., Xu, L., Liu, K., et al.: Recurrent convolutional neural networks for text classification. In: Twenty-Ninth AAAI Conference on Artificial Intelligence, pp. 121–123, July 2015

    Google Scholar 

  15. Cheng, Y., Ye, Z.M., Wang, M.M., et al.: Sentiment orientation analysis of Chinese text based on convolutional neural network and hierarchical attention network. Acta Sinica Sinica 33(01), 37–55 (2019)

    Google Scholar 

  16. Fu, R., Zhang, Z., Li, L.: Using LSTM and GRU neural network methods for traffic flow prediction. In: 2016 31st Youth Academic Annual Conference of Chinese Association of Automation, pp. 56–63, 2016

    Google Scholar 

  17. Schumaker, R.P., Chen, H.: Textual analysis of stock market prediction using breaking financial news: the AZF in text system. ACM Trans. Inf. Syst. (TOIS) 27(2), 11–15 (2009)

    Article  Google Scholar 

  18. Liu, X.T., Xu, X.F.: Can internet search behavior help us predict macro-economy? Econ. Res. 50(12), 68–83 (2015)

    Google Scholar 

  19. Lin, C.J., Li, Y.: Big data analysis still needs statistical thinking – taking Argo model as an example. Stat. Res. 33(11), 109–112 (2016)

    Google Scholar 

  20. Akita, R., Yoshihara, A., Matsubara, T., et al.: Deep learning for stock prediction using numerical and textual information. In: 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS), pp. 27–35, August 2016

    Google Scholar 

  21. Xu, Y.M., Gao, Y.M.: Construction and application of CPI public opinion index based on internet big data – taking Baidu index as an example. J. Quant. Tech. Econ. 034(001), 94–112 (2017)

    Google Scholar 

  22. Xu, Y., Cohen, S.B.: Stock movement prediction from tweets and historical prices. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 34–36, June 2018

    Google Scholar 

  23. Liu, G., Wang, X.: A numerical-based attention method for stock market prediction with dual information. IEEE Access 7, 7357–7367 (2019)

    Article  Google Scholar 

  24. Zhao, J.H., Li, Y.H., Huo, L., et al.: Macroeconomic prediction method integrating micro blog sentiment analysis and deep learning. Comput. Appl. 38(11), 11–16 (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, Hubei Province, China

    Yao Chen

  2. School of Mathematics and Statistics, Hubei Minzu University, Enshi, Hubei Province, China

    Yao Chen

Authors
  1. Yao Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yao Chen .

Editor information

Editors and Affiliations

  1. School of Computer Science, University of Oklahoma, Norman, OK, USA

    Mohammed Atiquzzaman

  2. University of Aizu, Fukushima, Japan

    Neil Yen

  3. Shanghai University of Medicine and Health Sciences, Shanghai, China

    Zheng Xu

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chen, Y. (2021). Review and Prospect of Text Analysis Based on Deep Learning and Its Application in Macroeconomic Forecasting. In: Atiquzzaman, M., Yen, N., Xu, Z. (eds) Big Data Analytics for Cyber-Physical System in Smart City. BDCPS 2020. Advances in Intelligent Systems and Computing, vol 1303. Springer, Singapore. https://doi.org/10.1007/978-981-33-4572-0_61

Download citation

  • DOI: https://doi.org/10.1007/978-981-33-4572-0_61

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-33-4573-7

  • Online ISBN: 978-981-33-4572-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation