Chinese Font Recommendation Based on Emotional Adjectives

Tzren Ru Chou, Jiaxuan Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A recommendation system is designed for users to make better choices and find what they need. At present, there are few studies on the design of recommendation systems for Chinese fonts based on emotional adjectives. Therefore, this study proposes a recommendation method for Chinese fonts based on emotional adjectives and establishes an emotional classification algorithm as well as a font assignment module, which delivers different font results according to the emotional descriptive sentences that are typed in. The method proposed by this study has demonstrated good results in preliminary experiments. A complete experiment will be conducted to verify the effect of this recommendation method for Chinese fonts.

Original languageEnglish
Title of host publicationProceedings of the 2020 4th International Conference on Innovation in Artificial Intelligence, ICIAI 2020
PublisherAssociation for Computing Machinery
Pages155-158
Number of pages4
ISBN (Electronic)9781450376587
DOIs
Publication statusPublished - 2020 May 8
Event4th International Conference on Innovation in Artificial Intelligence, ICIAI 2020 - Xiamen, China
Duration: 2020 May 82020 May 11

Publication series

NameACM International Conference Proceeding Series

Conference

Conference4th International Conference on Innovation in Artificial Intelligence, ICIAI 2020
CountryChina
CityXiamen
Period20/5/820/5/11

Keywords

  • emotional adjectives
  • Font recommendation

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

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  • Cite this

    Chou, T. R., & Lin, J. (2020). Chinese Font Recommendation Based on Emotional Adjectives. In Proceedings of the 2020 4th International Conference on Innovation in Artificial Intelligence, ICIAI 2020 (pp. 155-158). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3390557.3394131