{"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/social-emotion-mining-techniques-for-facebook","title":"Social Emotion Mining Techniques for Facebook Posts Reaction Prediction","arxiv_id":"1712.03249","date":"2017-12-08","proceeding":null,"authors":["Florian Krebs","Bruno Lubascher","Tobias Moers","Pieter Schaap","Gerasimos Spanakis"],"abstract":"As of February 2016 Facebook allows users to express their experienced\nemotions about a post by using five so-called `reactions'. This research paper\nproposes and evaluates alternative methods for predicting these reactions to\nuser posts on public pages of firms/companies (like supermarket chains). For\nthis purpose, we collected posts (and their reactions) from Facebook pages of\nlarge supermarket chains and constructed a dataset which is available for other\nresearches. In order to predict the distribution of reactions of a new post,\nneural network architectures (convolutional and recurrent neural networks) were\ntested using pretrained word embeddings. Results of the neural networks were\nimproved by introducing a bootstrapping approach for sentiment and emotion\nmining on the comments for each post. The final model (a combination of neural\nnetwork and a baseline emotion miner) is able to predict the reaction\ndistribution on Facebook posts with a mean squared error (or misclassification\nrate) of 0.135.","url_abs":"http://arxiv.org/abs/1712.03249v1","url_pdf":"http://arxiv.org/pdf/1712.03249v1.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":"social-emotion-mining-techniques-for-facebook","repo_url":"https://github.com/jerryspan/FacebookR","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}