Large-scale visual sentiment ontology and detectors using adjective noun pairs

  • Authors:

  • Damian Borth;Rongrong Ji;Tao Chen;Thomas Breuel;Shih-Fu Chang

  • Affiliations:

  • University of Kaiserslautern, Kaiserslautern, Germany;Columbia University, New York, NY, USA;Columbia University, New York, NY, USA;University of Kaiserslautern, Kaiserslautern, Germany;Columbia University, New York, NY, USA

  • Venue:
  • Proceedings of the 21st ACM international conference on Multimedia
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

Abstract

We address the challenge of sentiment analysis from visual content. In contrast to existing methods which infer sentiment or emotion directly from visual low-level features, we propose a novel approach based on understanding of the visual concepts that are strongly related to sentiments. Our key contribution is two-fold: first, we present a method built upon psychological theories and web mining to automatically construct a large-scale Visual Sentiment Ontology (VSO) consisting of more than 3,000 Adjective Noun Pairs (ANP). Second, we propose SentiBank, a novel visual concept detector library that can be used to detect the presence of 1,200 ANPs in an image. The VSO and SentiBank are distinct from existing work and will open a gate towards various applications enabled by automatic sentiment analysis. Experiments on detecting sentiment of image tweets demonstrate significant improvement in detection accuracy when comparing the proposed SentiBank based predictors with the text-based approaches. The effort also leads to a large publicly available resource consisting of a visual sentiment ontology, a large detector library, and the training/testing benchmark for visual sentiment analysis.