InfoAffect: A Dataset for Affective Analysis of Infographics
This work addresses the scarcity of data for affective analysis in infographics, which is an incremental contribution to multimodal AI research.
The authors tackled the problem of affective analysis in infographics by introducing the InfoAffect dataset with 3.5k samples, achieving a high accuracy score of 0.986 on the Composite Affect Consistency Index.
Infographics are widely used to convey complex information, yet their affective dimensions remain underexplored due to the scarcity of data resources. We introduce a 3.5k-sample affect-annotated InfoAffect dataset, which combines textual content with real-world infographics. We first collect the raw data from six domains and aligned them via preprocessing, the accompanied-text-priority method, and three strategies to guarantee the quality and compliance. After that we construct an affect table and use it to constrain annotation. Five state-of-the-art multimodal large language models (MLLMs) then analyze both modalities, and their outputs are fused with Reciprocal Rank Fusion (RRF) algorithm to yield robust affects and confidences. We conducted a user study with two experiments to validate usability and assess InfoAffect dataset using the Composite Affect Consistency Index (CACI), achieving an overall score of 0.986, which indicates high accuracy.