Towards an Automatic Generation of Persuasive Messages

Edson Lipa-Urbina, Nelly Condori-Fernandez, Franci Suni-Lopez

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In the last decades, the Natural Language Generation (NLG) methods have been improved to generate text automatically. However, based on the literature review, there are not works on generating text for persuading people. In this paper, we propose to use the SentiGAN framework to generate messages that are classified into levels of persuasiveness. And, we run an experiment using the Microtext dataset for the training phase. Our preliminary results show 0.78 of novelty on average, and 0.57 of diversity in the generated messages.

Original languageEnglish
Title of host publicationPersuasive Technology - 16th International Conference, PERSUASIVE 2021, Proceedings
EditorsRaian Ali, Birgit Lugrin, Fred Charles
PublisherSpringer Science and Business Media Deutschland GmbH
Pages55-62
Number of pages8
ISBN (Print)9783030794590
DOIs
StatePublished - 2021
Event16th International Conference on Persuasive Technology, PERSUASIVE 2021 - Virtual, Online
Duration: 12 Apr 202114 Apr 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12684 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th International Conference on Persuasive Technology, PERSUASIVE 2021
CityVirtual, Online
Period12/04/2114/04/21

Bibliographical note

Funding Information:
Acknowledgments. This work has been supported by CONCYTEC - FONDECYT within the framework of the call E038-01 contract 014-2019. N. Condori Fernandez wish also to thank Datos 4.0 (TIN2016-78011-C4-1-R) funded by MINECO-AEI/FEDER-UE.

Funding Information:
This work has been supported by CONCYTEC-FONDECYT within the framework of the call E038-01 contract 014-2019. N. Condori Fernandez wish also to thank Datos 4.0 (TIN2016-78011-C4-1-R) funded by MINECO-AEI/FEDER-UE.

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Keywords

  • Persuasive message
  • SentiGAN
  • Text generation

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