{
  "id": "1411.5731",
  "version": "v1",
  "published": "2014-11-21T00:39:43.000Z",
  "updated": "2014-11-21T00:39:43.000Z",
  "title": "Visual Sentiment Prediction with Deep Convolutional Neural Networks",
  "authors": [
    "Can Xu",
    "Suleyman Cetintas",
    "Kuang-Chih Lee",
    "Li-Jia Li"
  ],
  "categories": [
    "cs.CV",
    "cs.NE",
    "stat.ML"
  ],
  "abstract": "Images have become one of the most popular types of media through which users convey their emotions within online social networks. Although vast amount of research is devoted to sentiment analysis of textual data, there has been very limited work that focuses on analyzing sentiment of image data. In this work, we propose a novel visual sentiment prediction framework that performs image understanding with Deep Convolutional Neural Networks (CNN). Specifically, the proposed sentiment prediction framework performs transfer learning from a CNN with millions of parameters, which is pre-trained on large-scale data for object recognition. Experiments conducted on two real-world datasets from Twitter and Tumblr demonstrate the effectiveness of the proposed visual sentiment analysis framework.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2014-11-21T00:39:43.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "deep convolutional neural networks",
      "visual sentiment prediction framework",
      "prediction framework performs transfer",
      "framework performs transfer learning"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2014arXiv1411.5731X"
    }
  }
}