Research Output
Affective Reasoning for Big Social Data Analysis
  This special section focuses on the introduction, presentation, and discussion of novel techniques that further develop and apply affective reasoning tools and techniques for big social data analysis. A key motivation for this special section, in particular, is to explore the adoption of novel affective reasoning frameworks and cognitive learning systems to go beyond a mere word-level analysis of natural language text and provide novel concept-level tools and techniques that allow a more efficient passage from (unstructured) natural language to (structured) machine-processable affective data, in potentially any domain. The selected papers aim to address the wide spectrum of issues related to affective computing research and, hence, better grasp the current limitations and opportunities related to this fast-evolving branch of artificial intelligence. Out of the 29 submissions received, 5 were accepted to appear in the special section. One of the accepted papers underwent 3 rounds of revisions, the rest were revised twice. The papers appearing in this issue are briefly summarized.

  • Type:


  • Date:

    27 November 2017

  • Publication Status:


  • DOI:


  • Funders:

    Historic Funder (pre-Worktribe)


Cambria, E., Hussain, A., & Vinciarelli, A. (2017). Affective Reasoning for Big Social Data Analysis. IEEE transactions on affective computing, 8(4), 426-427.


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