{"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/audio-visual-sentiment-analysis-for-learning","title":"Audio-Visual Sentiment Analysis for Learning Emotional Arcs in Movies","arxiv_id":"1712.02896","date":"2017-12-08","proceeding":null,"authors":["Eric Chu","Deb Roy"],"abstract":"Stories can have tremendous power -- not only useful for entertainment, they\ncan activate our interests and mobilize our actions. The degree to which a\nstory resonates with its audience may be in part reflected in the emotional\njourney it takes the audience upon. In this paper, we use machine learning\nmethods to construct emotional arcs in movies, calculate families of arcs, and\ndemonstrate the ability for certain arcs to predict audience engagement. The\nsystem is applied to Hollywood films and high quality shorts found on the web.\nWe begin by using deep convolutional neural networks for audio and visual\nsentiment analysis. These models are trained on both new and existing\nlarge-scale datasets, after which they can be used to compute separate audio\nand visual emotional arcs. We then crowdsource annotations for 30-second video\nclips extracted from highs and lows in the arcs in order to assess the\nmicro-level precision of the system, with precision measured in terms of\nagreement in polarity between the system's predictions and annotators' ratings.\nThese annotations are also used to combine the audio and visual predictions.\nNext, we look at macro-level characterizations of movies by investigating\nwhether there exist `universal shapes' of emotional arcs. In particular, we\ndevelop a clustering approach to discover distinct classes of emotional arcs.\nFinally, we show on a sample corpus of short web videos that certain emotional\narcs are statistically significant predictors of the number of comments a video\nreceives. These results suggest that the emotional arcs learned by our approach\nsuccessfully represent macroscopic aspects of a video story that drive audience\nengagement. Such machine understanding could be used to predict audience\nreactions to video stories, ultimately improving our ability as storytellers to\ncommunicate with each other.","url_abs":"http://arxiv.org/abs/1712.02896v1","url_pdf":"http://arxiv.org/pdf/1712.02896v1.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":"audio-visual-sentiment-analysis-for-learning","repo_url":"https://github.com/ucsd-dsc-arts/dsc160-midterm-group13","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}