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

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

Content Provider	ACM Digital Library
Author	Yang, Jianchao Jin, Hailin Luo, Jiebo You, Quanzeng
Abstract	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. We use sentimental queries to obtain half a million training samples from Getty Images. We have conducted extensive experiments on both machine weakly labeled and manually labeled image tweets. The results show that the proposed model can achieve better performance than the state-of-the-art textual and visual sentiment analysis algorithms alone.
Starting Page	13
Ending Page	22
Page Count	10
File Format	PDF
ISBN	9781450337168
DOI	10.1145/2835776.2835779
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-02-08
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Sentiment analysis Cross-modality regression Multimodality analysis
Content Type	Text
Resource Type	Article

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