期刊名称：International Journal of Signal Processing, Image Processing and Pattern Recognition
印刷版ISSN：2005-4254
出版年度：2016
卷号：9
期号：9
页码：205-214
出版社：SERSC
摘要：In modern society, visual content like images and videos is increasingly becoming anewformofmediato express users’ opinions on the Internet. As a complement to textual sentiment analysis, visual sentiment analysis intends to provide more robust information for data analytics by extractingemotion and sentiment toward topics and events from images and videos. Inspired byrecent works that applieddeep convolutional neural networks (CNN) to this challenging problem, we proposed a framework for image sentiment analysis with a novel deep neural network called Network in Network (NIN) which intends to improve the discriminability for local patches within receptive fields. We trained our network on a dataset consisting ofnearlyhalfamillion Flickr images and minimized the effect of noisy training data by fine-tuning the network in a progressive manner. Extensive experiments conducted on manually labeled Twitter images show that the proposed architecture performs better in visual sentiment analysis than conventional CNN and other traditional algorithms.
关键词：visual sentiment; deep learning; convolutional neural network; network in ;network
