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access icon free The Impact of Sentiment Orientations on Successful Crowdfunding Campaigns through Text Analytics

  • Author(s): Wei Wang 1 ; Kevin Zhu 2 ; Hongwei Wang 3 ; Yen-Chun Jim Wu 4
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  • Source: Volume 11, Issue 5, October 2017, p. 229 – 238
    DOI:  10.1049/iet-sen.2016.0295 , Print ISSN 1751-8806, Online ISSN 1751-8814
© The Institution of Engineering and Technology
Received 22/11/2016, Accepted 09/06/2017, Revised 13/05/2017, Published 14/06/2017

The sentiment implied in user generated content represents the authors' personality, attitude, education level and social status. In Crowdfunding, the sentimental factor of the text description may impact the backers' investment intention on the project. The authors study the textual description from the sentimental aspect on the pledge results by employing text mining. The study proves that positive sentiment in the blurb and detailed description promotes the successful campaigns while it should not contain any sentimental factor in title. The predictive analysis shows that the predictive accuracy can be improved 7% based on the baseline model after considering sentimental factors from 64.4% to 71.7%.

Inspec keywords: crowdsourcing; behavioural sciences computing; sentiment analysis; investment; data mining

Other keywords: social status; crowdfunding campaigns; education level; personality; investment intention; user generated content; attitude; text analytics; text description; predictive analysis; sentimental factors; sentiment orientations; text mining

Subjects: Financial computing; Social and behavioural sciences computing; Document processing and analysis techniques; Knowledge engineering techniques

References

    1. 1)
      • 13. Mollick, E.: ‘The dynamics of Crowdfunding: an exploratory study’, J. Bus. Venturing, 2014, 29, (1), pp. 1–16.
    2. 2)
      • 5. Qazi, A., Syed, K.B.S., Raj, R.G., et al: ‘A concept-level approach to the analysis of online review helpfulness’, Comput. Hum. Behav., 2016, 58, pp. 75–81.
    3. 3)
      • 10. Kastrati, Z., Imran, A.S., Yildirim-Yayilgan, S.: ‘SEMCON: a semantic and contextual objective metric for enriching domain ontology concepts’, Int. J. Semantic Web Inf. Syst. (IJSWIS), 2016, 12, (2), pp. 1–24.
    4. 4)
      • 9. Yin, P., Wang, H., Guo, K.: ‘Feature–opinion pair identification of product reviews in Chinese: a domain ontology modeling method’, New Rev. Hypermed. Multimed., 2013, 19, (1), pp. 3–24.
    5. 5)
      • 19. Li, X., Xie, H., Wang, R., et al: ‘Empirical analysis: stock market prediction via extreme learning machine’, Neural Comput. Appl., 2016, 27, (1), pp. 67–78.
    6. 6)
      • 6. Wu, Y.C., Shen, J.P., Chang, C.L.: ‘Electronic service quality of Facebook social commerce and collaborative learning’, Comput. Hum. Behav., 2015, 53, pp. 1395–1402.
    7. 7)
      • 16. Chen, L., Qi, L., Wang, F.: ‘Comparison of feature-level learning methods for mining online consumer reviews’, Expert Syst. Appl., 2012, 39, (10), pp. 9588–9601.
    8. 8)
      • 7. Taylor, S.E., Brown, J.D.: ‘Illusion and Well-being: A Social Psychological Perspective on Mental Health’, Psychol. Bull., 1988, 103, (2), p. 193.
    9. 9)
      • 1. Wu, Y.C., Chang, W.H., Yuan, C.H.: ‘Do Facebook profile pictures reflect user's personality?’, Comput. Hum. Behav., 2015, 53, pp. 880–889.
    10. 10)
      • 18. Dong, Y., Tao, D., Li, X., et al: ‘Texture classification and retrieval using shearlets and linear regression’, IEEE Trans. Cybern., 2015, 45, (3), pp. 358–369.
    11. 11)
      • 3. ‘Nielson A C. GLOBAL TRUST IN ADVERTISING’. Available at http://www.nielsen.com/us/en/insights/reports/2015/global-trust-in-advertising-2015.html, accessed 28 September 2015.
    12. 12)
      • 15. Xia, Y., Cambria, E., Hussain, A., et al: ‘Word polarity disambiguation using Bayesian model and opinion-level features’, Cogn. Comput., 2015, 7, (3), pp. 369–380.
    13. 13)
      • 12. Tang, C., Guo, L.: ‘Digging for gold with a simple tool: validating text mining in studying electronic word-of-mouth (eWOM) communication’, Mark. Lett., 2015, 26, (1), pp. 67–80.
    14. 14)
      • 21. Xiong, G., Bharadwaj, S.: ‘Prerelease buzz evolution patterns and new product performance’, Mark. Sci., 2014, 33, (3), pp. 401–421.
    15. 15)
      • 8. Gao, Q., Lin, M.: ‘Linguistic features and peer-to-peer loan quality: a machine learning approach’, Soc. Sci. Res. Netw., 2013, 14, pp. 1–58..
    16. 16)
      • 17. Liu, B.: ‘Web data mining’ (Springer-Verlag, Berlin, Heidelberg, 2007).
    17. 17)
      • 2. Zheng, W., Yuan, C.H., Chang, W.H., et al: ‘Profile pictures on social media: gender and regional differences’, Comput. Hum. Behav., 2016, 63, pp. 891–898.
    18. 18)
      • 4. Zhu, F., Zhang, X.: ‘Impact of online consumer reviews on sales: The moderating role of product and consumer characteristics’, J. Mark., 2010, 74, (2), pp. 133–148.
    19. 19)
      • 14. Colombo, M.G., Franzoni, C., Rossi-Lamastra, C.: ‘Internal social capital and the attraction of early contributions in Crowdfunding’, Entrep. Theory Pract., 2015, 39, (1), pp. 75–100.
    20. 20)
      • 20. Eliashberg, J., Hui, S.K., Zhang, Z.J.: ‘From story line to box office: A new approach for green-lighting movie scripts’, Manage. Sci., 2007, 53, (6), pp. 881–893.
    21. 21)
      • 11. Lee, G., Raghu, T.S.: ‘Determinants of mobile apps’ success: evidence from the app store market’, J. Manage. Inf. Syst., 2014, 31, (2), pp. 133–170.
    22. 22)
      • 22. Beliga, S., Meštrović, A., Martinčić-Ipšić, S.: ‘Selectivity-based keyword extraction method’, Int. J. Semantic Web Inf. Syst. (IJSWIS), 2016, 12, (3), pp. 1–26.
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