Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Intelligent Garbage Classification System Based on Open MV Read chapter Read first chapter

2021 | OriginalPaper | Chapter

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

Author : Yao Chen

Published in: Big Data Analytics for Cyber-Physical System in Smart City

Publisher: Springer Singapore

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter A Brief Analysis of Wearable Electronic Medical Devices
next chapter The Research on the Construction of the “Online + Offline” Hybrid Teaching Mode of College English Under “Internet +” Background
Literature
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
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)CrossRef
3.
Manela, A., Moreira, A.: News implied volatility and disaster concerns. J. Financ. Econ. 123(1), 19–29 (2017)CrossRef
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)
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)
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
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)MathSciNetCrossRef
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
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)
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
11.
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313, 57–86 (2006)MathSciNetCrossRef
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)CrossRef
13.
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
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)
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
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)CrossRef
18.
Liu, X.T., Xu, X.F.: Can internet search behavior help us predict macro-economy? Econ. Res. 50(12), 68–83 (2015)
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)
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
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)
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
23.
Liu, G., Wang, X.: A numerical-based attention method for stock market prediction with dual information. IEEE Access 7, 7357–7367 (2019)CrossRef
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)
Metadata
Title
Review and Prospect of Text Analysis Based on Deep Learning and Its Application in Macroeconomic Forecasting
Author
Yao Chen
Copyright Year
2021
Publisher
Springer Singapore
Book
Big Data Analytics for Cyber-Physical System in Smart City

Print ISBN: 978-981-334-573-7

Electronic ISBN: 978-981-334-572-0

Copyright Year: 2021

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

Premium Partner

    Image Credits