计算机科学
情绪分析
卷积神经网络
人工智能
社会化媒体
任务(项目管理)
深度学习
可视化
机器学习
模式识别(心理学)
万维网
经济
管理
作者
Víctor Campos,Brendan Jou,Xavier Giró-i-Nieto
出处
期刊:Cornell University - arXiv
日期:2016-04-12
被引量:3
摘要
Visual multimedia have become an inseparable part of our digital social lives, and they often capture moments tied with deep affections. Automated visual sentiment analysis tools can provide a means of extracting the rich feelings and latent dispositions embedded in these media. In this work, we explore how Convolutional Neural Networks (CNNs), a now de facto computational machine learning tool particularly in the area of Computer Vision, can be specifically applied to the task of visual sentiment prediction. We accomplish this through fine-tuning experiments using a state-of-the-art CNN and via rigorous architecture analysis, we present several modifications that lead to accuracy improvements over prior art on a dataset of images from a popular social media platform. We additionally present visualizations of local patterns that the network learned to associate with image sentiment for insight into how visual positivity (or negativity) is perceived by the model.
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