42,808 research outputs found
From Pixels to Sentiment: Fine-tuning CNNs for Visual Sentiment Prediction
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.Comment: Accepted for publication in Image and Vision Computing. Models and
source code available at https://github.com/imatge-upc/sentiment-201
Diving Deep into Sentiment: Understanding Fine-tuned CNNs for Visual Sentiment Prediction
Visual media are powerful means of expressing emotions and sentiments. The
constant generation of new content in social networks highlights the need of
automated visual sentiment analysis tools. While Convolutional Neural Networks
(CNNs) have established a new state-of-the-art in several vision problems,
their application to the task of sentiment analysis is mostly unexplored and
there are few studies regarding how to design CNNs for this purpose. In this
work, we study the suitability of fine-tuning a CNN for visual sentiment
prediction as well as explore performance boosting techniques within this deep
learning setting. Finally, we provide a deep-dive analysis into a benchmark,
state-of-the-art network architecture to gain insight about how to design
patterns for CNNs on the task of visual sentiment prediction.Comment: Preprint of the paper accepted at the 1st Workshop on Affect and
Sentiment in Multimedia (ASM), in ACM MultiMedia 2015. Brisbane, Australi
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