@inproceedings{mathews2016senticap,
  title        = {{SentiCap: Generating Image Descriptions with Sentiments}},
  author       = {Mathews, Alexander and Xie, Lexing and He, Xuming},
  booktitle    = {Thirtieth {AAAI} Conference on Artificial Intelligence ({AAAI-16})},
  url_abstract = {http://arxiv.org/abs/1510.01431},
  url_paper    = {http://arxiv.org/pdf/1510.01431v2.pdf},
  url_slides   = {http://cm.cecs.anu.edu.au/documents/senticap_slides.pdf},
  address      = {Phoenix, Arizona USA},
  year         = {2016},
  abstract     = {The recent progress on image recognition and language modeling is making automatic description of image content a reality. However, stylized, non-factual aspects of the written description are missing from the current systems. One such style is descriptions with emotions, which is commonplace in everyday communication, and influences decision-making and interpersonal relationships. We design a system to describe an image with emotions, and present a model that automatically generates captions with positive or negative sentiments. We propose a novel switching recurrent neural network with word-level regularization, which is able to produce emotional image captions using only 2000+ training sentences containing sentiments. We evaluate the captions with different automatic and crowd-sourcing metrics. Our model compares favourably in common quality metrics for image captioning. In 84.6\% of cases the generated positive captions were judged as being at least as descriptive as the factual captions. Of these positive captions 88\% were confirmed by the crowd-sourced workers as having the appropriate sentiment.}
}

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