Cross-modality Consistent Regression for Joint Visual-Textual Sentiment Analysis of Social Multimedia

Sentiment analysis of online user generated content is important for many social media analytics tasks. Researchers have largely relied on textual sentiment analysis to develop systems to predict political elections, measure economic indicators, and so on. Recently, social media users are increasingly using additional images and videos to express their opinions and share their experiences. Sentiment analysis of such large-scale textual and visual content can help better extract user sentiments toward events or topics. Motivated by the needs to leverage large-scale social multimedia content for sentiment analysis, we propose a cross-modality consistent regression (CCR) model, which is able to utilize both the state-of-the-art visual and textual sentiment analysis techniques. We first fine-tune a convolutional neural network (CNN) for image sentiment analysis and train a paragraph vector model for textual sentiment analysis. On top of them, we train our multi-modality regression model...

• Type: Journal
• Year: 2016
• Where: WSDM
• Authors: Quanzeng You, Jiebo Luo, Hailin Jin, Jianchao Yang
• Keywords: Data Mining, WSDM 2016