{"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/diving-deep-into-sentiment-understanding-fine","title":"Diving Deep into Sentiment: Understanding Fine-tuned CNNs for Visual Sentiment Prediction","arxiv_id":"1508.05056","date":"2015-08-20","proceeding":null,"authors":["Victor Campos","Amaia Salvador","Brendan Jou","Xavier Giró-i-Nieto"],"abstract":"Visual media are powerful means of expressing emotions and sentiments. The\nconstant generation of new content in social networks highlights the need of\nautomated visual sentiment analysis tools. While Convolutional Neural Networks\n(CNNs) have established a new state-of-the-art in several vision problems,\ntheir application to the task of sentiment analysis is mostly unexplored and\nthere are few studies regarding how to design CNNs for this purpose. In this\nwork, we study the suitability of fine-tuning a CNN for visual sentiment\nprediction as well as explore performance boosting techniques within this deep\nlearning setting. Finally, we provide a deep-dive analysis into a benchmark,\nstate-of-the-art network architecture to gain insight about how to design\npatterns for CNNs on the task of visual sentiment prediction.","url_abs":"http://arxiv.org/abs/1508.05056v2","url_pdf":"http://arxiv.org/pdf/1508.05056v2.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":"diving-deep-into-sentiment-understanding-fine","repo_url":"https://github.com/imatge-upc/sentiment-2015-asm","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"caffe2","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":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}