Sentiment Variations in Text for Persuasion Technology

  • Lorenzo Gatti
  • Marco Guerini
  • Oliviero Stock
  • Carlo Strapparava
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8462)


Accurate wording is essential in persuasive verbal communication. Through it speakers can provide an affective connotation to the text and reveal their disposition or induce a similar disposition on the recipient. All this is apparent in persuasion texts par excellence, such as political speech and advertisement. Automatic sentiment variations of existing linguistic expressions open the way to promising applications, yet it is a challenging problem. In this paper we describe a system which takes up this challenge, together with a framework for evaluating the persuasiveness of the newly produced expressions.


Language-based persuasion affective NLP persuasiveness evaluation 


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  1. 1.
    Aleksandrov, M., Strapparava, C.: NgramQuery - smart information extraction from Google N-gram using external resources. In: Proceedings of LREC 2012, Istanbul, Turkey, pp. 563–568 (2012)Google Scholar
  2. 2.
    Andrews, P., Manandhar, S., De Boni, M.: Argumentative human computer dialogue for automated persuasion. In: Proceedings of SIGdial Workshop on Discourse and Dialogue, pp. 138–147. ACL (2008)Google Scholar
  3. 3.
    Baccianella, S., Esuli, A., Sebastiani, F.: SentiWordNet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In: Proceedings of LREC 2010, Valletta, Malta, pp. 2200–2204 (2010)Google Scholar
  4. 4.
    Aral, S., Walker, D.: Creating social contagion through viral product design: A randomized trial of peer influence in networks. In: Proceedings of ICIS 2010 (2010)Google Scholar
  5. 5.
    Brants, T., Franz, A.: Web 1T 5-gram version 1. Linguistic Data Consortium (2006)Google Scholar
  6. 6.
    Carofiglio, V., de Rosis, F.: Combining logical with emotional reasoning in natural argumentation. In: Proceedings of the UM 2003 Workshop on Affect (2003)Google Scholar
  7. 7.
    Das, D., Schneider, N., Chen, D., Smith, N.A.: Probabilistic frame-semantic parsing. In: Proceedings of NAACL-HLT 2010, Los Angeles, USA, pp. 948–956 (2010)Google Scholar
  8. 8.
    De Marneffe, M.C., MacCartney, B., Manning, C.D.: Generating typed dependency parses from phrase structure parses. In: Proceedings of LREC 2006 (2006)Google Scholar
  9. 9.
    de Rosis, F., Grasso, F.: Affective natural language generation. In: Paiva, A.C.R. (ed.) IWAI 1999. LNCS (LNAI), vol. 1814, pp. 204–218. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  10. 10.
    Dumais, S.T.: Latent semantic analysis. Annual Review of Information Science and Technology 38(1), 188–230 (2004)CrossRefGoogle Scholar
  11. 11.
    Elliott, C.: Multi-media communication with emotion driven ’believable agents’. In: AAAI Technical Report for the Spring Symposium on Believeable Agents, pp. 16–20. Stanford University (1994)Google Scholar
  12. 12.
    Fellbaum, C.: Wordnet: An electronic database (1998)Google Scholar
  13. 13.
    Gardiner, M., Dras, M.: Valence shifting: Is it a valid task? In: Australasian Language Technology Association Workshop 2012, p. 42 (2012)Google Scholar
  14. 14.
    Giora, R., Fein, O., Kronrod, A., Elnatan, I., Shuval, N., Zur, A.: Weapons of mass distraction: Optimal innovation and pleasure ratings. Metaphor and Symbol 19(2), 115–141 (2004)CrossRefGoogle Scholar
  15. 15.
    Gmytrasiewicz, P.J., Lisetti, C.L.: Emotions and personality in agent design and modeling. In: Bauer, M., Gmytrasiewicz, P.J., Vassileva, J. (eds.) UM 2001. LNCS (LNAI), vol. 2109, p. 237. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  16. 16.
    Grasso, F., Cawsey, A., Jones, R.: Dialectical argumentation to solve conflicts in advice giving: a case study in the promotion of healthy nutrition. International Journal of Human-Computer Studies 53(6), 1077–1115 (2000)CrossRefzbMATHGoogle Scholar
  17. 17.
    Guerini, M., Strapparava, C., Stock, O.: CORPS: A corpus of tagged political speeches for persuasive communication processing. Journal of Information Technology & Politics 5(1), 19–32 (2008)CrossRefGoogle Scholar
  18. 18.
    Guerini, M., Gatti, L., Turchi, M.: Sentiment analysis: How to derive prior polarities from SentiWordNet. In: Proceedings of EMNLP 2013 (2013)Google Scholar
  19. 19.
    Guerini, M., Stock, O., Strapparava, C.: Valentino: A tool for valence shifting of natural language texts. In: Proceedings of LREC 2008, pp. 243–246 (2008)Google Scholar
  20. 20.
    Guerini, M., Strapparava, C., Stock, O.: Slanting existing text with valentino. In: Proceedings of IUI 2011, Palo Alto, USA, pp. 439–440 (2011)Google Scholar
  21. 21.
    Guerini, M., Strapparava, C., Stock, O.: Ecological evaluation of persuasive messages using google adwords. In: Proceedings of ACL 2012, pp. 988–996 (2012)Google Scholar
  22. 22.
    Hernault, H., Prendinger, H., Ishizuka, M., et al.: HILDA: a discourse parser using support vector machine classification. Dialogue & Discourse 1(3), 1–33 (2010)CrossRefGoogle Scholar
  23. 23.
    Kaptein, M., De Ruyter, B., Markopoulos, P., Aarts, E.: Adaptive persuasive systems: A study of tailored persuasive text messages to reduce snacking. ACM Transactions on Interactive Intelligent Systems 2(2), 10:1–10:25 (2012)Google Scholar
  24. 24.
    Kaptein, M., Halteren, A.: Adaptive persuasive messaging to increase service retention: using persuasion profiles to increase the effectiveness of email reminders. Personal and Ubiquitous Computing 17(6), 1173–1185 (2013)CrossRefGoogle Scholar
  25. 25.
    Lee, H., Chang, A., Peirsman, Y., Chambers, N., Surdeanu, M., Jurafsky, D.: Deterministic coreference resolution based on entity-centric, precision-ranked rules. Computational Linguistics 39(4), 1–54 (2013)CrossRefGoogle Scholar
  26. 26.
    Mason, W., Suri, S.: Conducting behavioral research on amazon’s mechanical turk. Behavior Research Methods, 1–23 (2010)Google Scholar
  27. 27.
    Mateas, M., Vanouse, P., Domike, S.: Generation of ideologically-biased historical documentaries. In: Proceedings of AAAI 2000, Austin, USA, pp. 236–242 (2000)Google Scholar
  28. 28.
    Negri, M., Bentivogli, L., Mehdad, Y., Giampiccolo, D., Marchetti, A.: Divide and conquer: Crowdsourcing the creation of cross-lingual textual entailment corpora. In: Proceedings of EMNLP 2011 (2011)Google Scholar
  29. 29.
    Ortony, A., Clore, G.L., Collins, A.: The cognitive structure of emotions. Cambridge University Press (1988)Google Scholar
  30. 30.
    Özbal, G., Strapparava, C.: A computational approach to automatize creative naming. In: Proceedings of ACL 2012, Jeju Island, Korea (2012)Google Scholar
  31. 31.
    Özbal, G., Pighin, D., Strapparava, C.: Brainsup: Brainstorming support for creative sentence generation. In: Proceedings of ACL 2013, Sofia, Bulgaria (2013)Google Scholar
  32. 32.
    Pianta, E., Girardi, C., Zanoli, R.: The TextPro tool suite. In: Proceedings of LREC 2008, pp. 2603–2607 (2008)Google Scholar
  33. 33.
    Piwek, P.: An annotated bibliography of affective natural language generation. ITRI ITRI-02-02, University of Brighton (2002)Google Scholar
  34. 34.
    Poggi, I.: A goal and belief model of persuasion. Pragmatics and Cognition (2004)Google Scholar
  35. 35.
    Rehm, M., Andrè, E.: Catch me if you can – exploring lying agents in social settings. In: Proceedings of AAMAS 2005, pp. 937–944 (2005)Google Scholar
  36. 36.
    Reiter, E., Dale, R.: Building Natural Language Generation Systems. Cambridge University Press (2000)Google Scholar
  37. 37.
    Reiter, E., Robertson, R., Osman, L.: Lesson from a failure: Generating tailored smoking cessation letters. Artificial Intelligence 144, 41–58 (2003)CrossRefGoogle Scholar
  38. 38.
    Sillince, J.A.A., Minors, R.H.: What makes a strong argument? emotions, highly-placed values and role playing. Communication and Cognition 24, 281–298 (1991)Google Scholar
  39. 39.
    Turney, P.D.: Mining the Web for synonyms: PMI-IR versus LSA on TOEFL. In: Proceedings of EMCL 2001, Freiburg, Germany, pp. 491–502 (2001)Google Scholar
  40. 40.
    van der Sluis, I., Mellish, C.: Towards empirical evaluation of affective tactical NLG. In: Krahmer, E., Theune, M. (eds.) Empirical Methods in NLG. LNCS (LNAI), vol. 5790, pp. 242–263. Springer, Heidelberg (2010)Google Scholar
  41. 41.
    Van Der Sluis, I., Mellish, C.: Towards empirical evaluation of affective tactical NLG. In: Proceedings of ENLG 2009, Athens, Greece, pp. 146–153 (2009)Google Scholar
  42. 42.
    Veale, T.: Creative language retrieval: A robust hybrid of information retrieval and linguistic creativity. In: Proceedings of HLT 2011, Portland, USA, pp. 278–287 (2011)Google Scholar
  43. 43.
    Whitehead, S., Cavedon, L.: Generating shifting sentiment for a conversational agent. In: Proceedings of NAACL-HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, pp. 89–97 (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Lorenzo Gatti
    • 1
  • Marco Guerini
    • 1
  • Oliviero Stock
    • 2
  • Carlo Strapparava
    • 2
  1. 1.Trento RISETrentoItaly
  2. 2.FBK-irstTrentoItaly

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