Skip to main content

Visualization of Topic-Sentiment Dynamics in Crowdfunding Projects

  • Conference paper
  • First Online:
Advances in Intelligent Data Analysis XVI (IDA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10584))

Included in the following conference series:

Abstract

We develop a model that connects the ideas of topic modeling and time series via the construction of topic-sentiment random variables. By doing so, the proposed model provides an easy-to-understand topic-sentiment relationship while also improving the accuracy of regression models on quantitative variables associated with texts. We perform empirical studies on crowdfunding, which has gained mainstream attention due to its enormous penetration in modern society via a variety of online crowdfunding platforms. We study Kickstarter, one of the major players in this market and propose a model and an inference procedure for the amount of money donated to projects and their likelihood of success by capturing and quantifying the importance (sentiment) that possible donors give to the subjects (topics) of the projects. Experiments on a set of 45 K projects show that the addition of the temporal elements adds valuable information to the regression model and allows for a better explanation of the overall temporal behavior of the whole market in Kickstarter.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Mollick, E.R.: Containing multitudes: the many impacts of kickstarter funding. Available at SSRN 2808000 (2016)

    Google Scholar 

  2. Greenberg, M.D., Pardo, B., Hariharan, K., Gerber, E.: Crowdfunding support tools: predicting success & failure. In: CHI 2013 Extended Abstracts on Human Factors in Computing Systems. ACM (2013)

    Google Scholar 

  3. Blei, D., Ng, A., Jordan, M.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  4. Durbin, J., Koopman, S.J.: Time Series Analysis by State Space Methods, vol. 38. OUP Oxford (2012)

    Google Scholar 

  5. Cragg, J.G.: Some statistical models for limited dependent variables with application to the demand for durable goods. Econometrica J. Econ. Soc. 39, 829–844 (1971)

    Article  MATH  Google Scholar 

  6. Jordan, M., Ghaharamani, Z., Jaakkola, T., Saul, L.: An introduction to variational methods for graphical models. In: Learning in Graphical Models, pp. 105–162 (1998)

    Google Scholar 

  7. Bernardo, J., Bayarri, M., Berger, J., Dawid, A., Heckerman, D., Smith, A., West, M., et al.: The variational bayesian em algorithm for incomplete data: with application to scoring graphical model structures. Bayesian Stat. 7, 453–464 (2003)

    MathSciNet  Google Scholar 

  8. Beal, M.J., Ghahramani, Z.: The variational kalman smoother. Gatsby Computational Neuroscience Unit, University College London, Technical report (2001)

    Google Scholar 

  9. Greene, W.H.: Econometric Analysis. Pearson Education India (2003)

    Google Scholar 

  10. Consonni, G., Marin, J.M.: Mean-field variational approximate bayesian inference for latent variable models. Comput. Stat. Data Anal. 52(2), 790–798 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  11. Jaakkola, T.S., Qi, Y.: Parameter expanded variational bayesian methods. In: Advances in Neural Information Processing Systems, pp. 1097–1104 (2006)

    Google Scholar 

  12. Kuppuswamy, V., Bayus, B.L.: Crowdfunding creative ideas: the dynamics of project backers in kickstarter. UNC Kenan-Flagler Research Paper (2015)

    Google Scholar 

  13. Wang, C., Paisley, J., Blei, D.: Online variational inference for the hierarchical dirichlet process. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp. 752–760 (2011)

    Google Scholar 

  14. Zhang, C., Kjellström, H.: How to supervise topic models. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014. LNCS, vol. 8926, pp. 500–515. Springer, Cham (2015). doi:10.1007/978-3-319-16181-5_39

    Google Scholar 

  15. Blei, D.M., McAuliffe, J.D.: Supervised Topic Models. In: NIPS - Advances in Neural Information Processing Systems, pp. 121–128 (2008)

    Google Scholar 

  16. Rabinovich, M., Blei, D.M.: The inverse regression topic model. In: ICML - International Conference on Machine Learning, pp. 199–207 (2014)

    Google Scholar 

  17. Ramage, D., Hall, D., Nallapati, R., Manning, C.D.: Labeled LDA: a supervised topic model for credit attribution in multi-labeled corpora. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing (2009)

    Google Scholar 

  18. Park, S., Lee, W., Moon, I.C.: Supervised dynamic topic models for associative topic extraction with a numerical time series. In: CIKM - International Conference on Information and Knowledge Management, pp. 49–54 (2015)

    Google Scholar 

  19. Etter, V., Grossglauser, M., Thiran, P.: Launch hard or go home!: predicting the success of kickstarter campaigns. In: Proceedings of the First ACM Conference on Online Social Networks, pp. 177–182. ACM (2013)

    Google Scholar 

  20. Chen, K., Jones, B., Kim, I., Schlamp, B.: Kickpredict: Predicting kickstarter success (2013)

    Google Scholar 

  21. Kamath, R., Kamat, R.: Supervised learning model for kickstarter campaigns with rmining (2016)

    Google Scholar 

  22. Rakesh, V., Choo, J., Reddy, C.K.: Project recommendation using heterogeneous traits in crowdfunding. In: International AAAI Conference on Web and Social Media (2015)

    Google Scholar 

  23. Mitra, T., Gilbert, E.: The language that gets people to give: phrases that predict success on kickstarter. In: Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing, pp. 49–61. ACM (2014)

    Google Scholar 

  24. Althoff, T., Leskovec, J.: Donor retention in online crowdfunding communities: a case study of donorschoose.org. In: Proceedings of the 24th International Conference on World Wide Web, pp. 34–44. ACM (2015)

    Google Scholar 

  25. An, J., Quercia, D., Crowcroft, J.: Recommending investors for crowdfunding projects. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 261–270. ACM (2014)

    Google Scholar 

Download references

Acknowledgments

Rafael Carmo was supported by Capes - Science Without Borders Programme (Process 99999.001034/2013-08) - Brazil.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rafael A. F. do Carmo .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

do Carmo, R.A.F., Kang, S.M., Silva, R. (2017). Visualization of Topic-Sentiment Dynamics in Crowdfunding Projects. In: Adams, N., Tucker, A., Weston, D. (eds) Advances in Intelligent Data Analysis XVI. IDA 2017. Lecture Notes in Computer Science(), vol 10584. Springer, Cham. https://doi.org/10.1007/978-3-319-68765-0_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-68765-0_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68764-3

  • Online ISBN: 978-3-319-68765-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics