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Sentiment analysis of online user generated content is important for manysocial media analytics tasks. Researchers have largely relied on textualsentiment analysis to develop systems to predict political elections, measureeconomic indicators, and so on. Recently, social media users are increasinglyusing images and videos to express their opinions and share their experiences.Sentiment analysis of such large scale visual content can help better extractuser sentiments toward events or topics, such as those in image tweets, so thatprediction of sentiment from visual content is complementary to textualsentiment analysis. Motivated by the needs in leveraging large scale yet noisytraining data to solve the extremely challenging problem of image sentimentanalysis, we employ Convolutional Neural Networks (CNN). We first design asuitable CNN architecture for image sentiment analysis. We obtain half amillion training samples by using a baseline sentiment algorithm to labelFlickr images. To make use of such noisy machine labeled data, we employ aprogressive strategy to fine-tune the deep network. Furthermore, we improve theperformance on Twitter images by inducing domain transfer with a small numberof manually labeled Twitter images. We have conducted extensive experiments onmanually labeled Twitter images. The results show that the proposed CNN canachieve better performance in image sentiment analysis than competingalgorithms.

Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks