{"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/multimodal-sentiment-analysis-to-explore-the","title":"Multimodal Sentiment Analysis To Explore the Structure of Emotions","arxiv_id":"1805.10205","date":"2018-05-25","proceeding":"ICLR 2018 1","authors":["Anthony Hu","Seth Flaxman"],"abstract":"We propose a novel approach to multimodal sentiment analysis using deep\nneural networks combining visual analysis and natural language processing. Our\ngoal is different than the standard sentiment analysis goal of predicting\nwhether a sentence expresses positive or negative sentiment; instead, we aim to\ninfer the latent emotional state of the user. Thus, we focus on predicting the\nemotion word tags attached by users to their Tumblr posts, treating these as\n\"self-reported emotions.\" We demonstrate that our multimodal model combining\nboth text and image features outperforms separate models based solely on either\nimages or text. Our model's results are interpretable, automatically yielding\nsensible word lists associated with emotions. We explore the structure of\nemotions implied by our model and compare it to what has been posited in the\npsychology literature, and validate our model on a set of images that have been\nused in psychology studies. Finally, our work also provides a useful tool for\nthe growing academic study of images - both photographs and memes - on social\nnetworks.","url_abs":"http://arxiv.org/abs/1805.10205v1","url_pdf":"http://arxiv.org/pdf/1805.10205v1.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":"multimodal-sentiment-analysis-to-explore-the","repo_url":"https://github.com/anthonyhu/tumblr-emotions","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"multimodal-sentiment-analysis-to-explore-the","repo_url":"https://github.com/deepsentiment/deepsentiment","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"multimodal-sentiment-analysis","task_name":"Multimodal Sentiment Analysis"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1805.10205","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}