Building Image Sentiment Dataset with an Online Rating Game
Chanhee Yoon, Keumhee Kang, Eun Yi Kim · 2015
In this paper, an online rating game called Image-Battle is developed to build the ground truth dataset for image sentiment analysis. Our goal is to provide more interesting and intuitive interface to users and to collect the images with more correct sentiment scores despite of less human intervention. For this, two schemes are designed: 1) a pair-wise competition and 2) a ranking algorithm based on visual link analysis. First, the system shows two images and asks the user which image is closer to a given sentiment. Thereafter, the ranking algorithm assigns the sentiment scores to the images based on all the competition results: the main idea is to give higher score to images that win more or to the images that beat images with high scores. To evaluate the proposed system, it was used to collect ground truth for 30,000 Photo.net images, each of which was labeled by six emotions. The ground truth was used to develop the sentiment recognition system, and its result was compared with that of other rating system. Then the result proved the excellence of the proposed method in terms of accuracy and user satisfaction.