{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/from-pixels-to-sentiment-fine-tuning-cnns-for","title":"From Pixels to Sentiment: Fine-tuning CNNs for Visual Sentiment Prediction","arxiv_id":"1604.03489","date":"2016-04-12","proceeding":null,"authors":["Victor Campos","Brendan Jou","Xavier Giro-i-Nieto"],"abstract":"Visual multimedia have become an inseparable part of our digital social\nlives, and they often capture moments tied with deep affections. Automated\nvisual sentiment analysis tools can provide a means of extracting the rich\nfeelings and latent dispositions embedded in these media. In this work, we\nexplore how Convolutional Neural Networks (CNNs), a now de facto computational\nmachine learning tool particularly in the area of Computer Vision, can be\nspecifically applied to the task of visual sentiment prediction. We accomplish\nthis through fine-tuning experiments using a state-of-the-art CNN and via\nrigorous architecture analysis, we present several modifications that lead to\naccuracy improvements over prior art on a dataset of images from a popular\nsocial media platform. We additionally present visualizations of local patterns\nthat the network learned to associate with image sentiment for insight into how\nvisual positivity (or negativity) is perceived by the model.","url_abs":"http://arxiv.org/abs/1604.03489v2","url_pdf":"http://arxiv.org/pdf/1604.03489v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"from-pixels-to-sentiment-fine-tuning-cnns-for","repo_url":"https://github.com/imatge-upc/sentiment-2017-imavis","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"caffe2","reach":null},{"paper_slug":"from-pixels-to-sentiment-fine-tuning-cnns-for","repo_url":"https://github.com/ronaldraxon/dauruxu","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"visual-sentiment-prediction","task_name":"Visual Sentiment Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}