Storytelling with Adjustable Narrator Styles and Sentiments

  • Boyang Li
  • Mohini Thakkar
  • Yijie Wang
  • Mark O. Riedl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8832)


Most storytelling systems to date rely on manually coded knowledge, the cost of which usually restricts such systems to operate within a few domains where knowledge has been engineered. Open Story Generation systems are capable of learning knowledge necessary for telling stories in a given domain. In this paper, we describe a technique that generates and communicates stories in language with diverse styles and sentiments based on automatically learned narrative knowledge. Diversity in storytelling style may facilitate different communicative goals and focalization in narratives. Our approach learns from large-scale data sets such as the Google N-Gram Corpus and Project Gutenberg books in addition to crowdsourced stories to instill storytelling agents with linguistic and social behavioral knowledge. A user study shows our algorithm strongly agrees with human judgment on the interestingness, conciseness, and sentiments of the generated stories and outperforms existing algorithms.


Target Word Plot Graph Negative Sentence Sentiment Dictionary Individual Sentence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Baccianella, S., Esuli, A., Sebastani, F.: SentiWordNet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In: The 7th Conference on International Language Resources and Evaluation (2010)Google Scholar
  2. 2.
    Bae, B.C., Cheong, Y.G., Young, R.M.: Automated story generation with multiple internal focalization. In: 2011 IEEE Conference on Computational Intelligence and Games, pp. 211–218 (2011)Google Scholar
  3. 3.
    Gervás, P.: Computational approaches to storytelling and creativity. AI Magazine 30, 49–62 (2009)Google Scholar
  4. 4.
    Li, B., Lee-Urban, S., Appling, D.S., Riedl, M.O.: Crowdsourcing narrative intelligence. Advances in Cognitive Systems 2 (2012)Google Scholar
  5. 5.
    Li, B., Lee-Urban, S., Johnston, G., Riedl, M.O.: Story generation with crowdsourced plot graphs. In: The 27th AAAI Conference on Artificial Intelligence (2013)Google Scholar
  6. 6.
    Mairesse, F., Walker, M.: Towards personality-based user adaptation: Psychologically informed stylistic language generation. User Modeling and User-Adapted Interaction 20, 227–278 (2010)CrossRefGoogle Scholar
  7. 7.
    McIntyre, N., Lapata, M.: Plot induction and evolutionary search for story generation. In: The 48th Annual Meeting of the Association for Computational Linguistics, pp. 1562–1572 (2010)Google Scholar
  8. 8.
    Michel, J.B., Shen, Y., Aiden, A., Veres, A., Gray, M., Brockman, W., The Google Books Team, Pickett, J., Hoiberg, D., Clancy, D., Norvig, P., Orwant, J., Pinker, S., Nowak, M., Aiden, E.: Quantitative analysis of culture using millions of digitized books. Science 331, 176–182 (2011)Google Scholar
  9. 9.
    Miller, G.: WordNet: A lexical database for English. Communications of the ACM 38, 39–41 (1995)CrossRefGoogle Scholar
  10. 10.
    Montfort., N.: Generating narrative variation in interactive fiction. Ph.D. thesis, University of Pennsylvania (2007)Google Scholar
  11. 11.
    Porteous, J., Cavazza, M., Charles, F.: Narrative generation through characters point of view. In: The SIGCHI Conference on Human Factors in Computing Systems (2010)Google Scholar
  12. 12.
    Riedl, M.O., Bulitko, V.: Interactive narrative: An intelligent systems approach. AI Magazine 34, 67–77 (2013)Google Scholar
  13. 13.
    Rishes, E., Lukin, S.M., Elson, D.K., Walker, M.A.: Generating different story tellings from semantic representations of narrative. In: Koenitz, H., Sezen, T.I., Ferri, G., Haahr, M., Sezen, D., C̨atak, G. (eds.) ICIDS 2013. LNCS, vol. 8230, pp. 192–204. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  14. 14.
    Sina, S., Rosenfeld, A., Kraus, S.: Generating content for scenario-based serious-games using crowdsourcing. In: The 28th AAAI Conference on Artificial Intelligence (2014)Google Scholar
  15. 15.
    Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C., Ng, A., Potts, C.: Recursive deep models for semantic compositionality over a sentiment treebank. In: The Conference on Empirical Methods in Natural Language Processing (2013)Google Scholar
  16. 16.
    Swanson, R., Gordon, A.S.: Say anything: Using textual case-based reasoning to enable open-domain interactive storytelling. ACM Transactions on Interactive Intelligent Systems 2, 1–35 (2012)Google Scholar
  17. 17.
    Toutanova, K., Klein, D., Manning, C., Singer, Y.: Feature-rich part-of-speech tagging with a cyclic dependency network. In: The NAACL-HLT Conference (2003)Google Scholar
  18. 18.
    Zhu, J., Ontañón, S., Lewter, B.: Representing game characters’ inner worlds through narrative perspectives. In: The 6th International Conference on Foundations of Digital Games, pp. 204–210 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Boyang Li
    • 1
  • Mohini Thakkar
    • 1
  • Yijie Wang
    • 1
  • Mark O. Riedl
    • 1
  1. 1.School of Interactive ComputingGeorgia Institute of TechnologyAtlantaUSA

Personalised recommendations