{"id":16778785,"url":"https://github.com/omarsar/meda","last_synced_at":"2026-01-03T22:05:50.267Z","repository":{"id":151625490,"uuid":"59543190","full_name":"omarsar/meda","owner":"omarsar","description":":smile: :worried: :joy: An emotion classification web service :smile: :worried: :joy:","archived":false,"fork":false,"pushed_at":"2016-06-12T04:20:08.000Z","size":44,"stargazers_count":5,"open_issues_count":0,"forks_count":2,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-01-23T06:22:27.343Z","etag":null,"topics":["classification-task","emotion-analysis","emotion-analytics","emotion-api","graph-theory","machine-learning","sentiment-analysis"],"latest_commit_sha":null,"homepage":"http://bit.ly/ilmeda","language":"HTML","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/omarsar.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2016-05-24T05:29:25.000Z","updated_at":"2022-09-09T05:36:48.000Z","dependencies_parsed_at":"2023-05-25T07:30:14.567Z","dependency_job_id":null,"html_url":"https://github.com/omarsar/meda","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/omarsar%2Fmeda","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/omarsar%2Fmeda/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/omarsar%2Fmeda/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/omarsar%2Fmeda/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/omarsar","download_url":"https://codeload.github.com/omarsar/meda/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243922514,"owners_count":20369405,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["classification-task","emotion-analysis","emotion-analytics","emotion-api","graph-theory","machine-learning","sentiment-analysis"],"created_at":"2024-10-13T07:28:41.088Z","updated_at":"2026-01-03T22:05:50.197Z","avatar_url":"https://github.com/omarsar.png","language":"HTML","readme":"## :point_right: An Multilingual Emotion Classification Web Service\nThis is a simple web service used to classify text into its pertaining emotion. It works with languages such as English, Spanish and French. A Research Project by [IDEALab](https://github.com/IDEA-NTHU-Taiwan). Presented in [ASONAM 2015](http://asonam.cpsc.ucalgary.ca/2015/)\n\n# :point_right: Abstract\nTraditional classifiers require extracting high dimensional feature representations, which become computationally expensive to process and can misrepresent or deteriorate the accuracy of a classifier. By utilizing a more representative list of extracted patterns, we can improve the precision and recall of a classification task. In this paper, we propose an unsupervised graph-based approach for bootstrapping Twitter-specific emotion-bearing patterns. Due to its novel bootstrapping process, the full system is also adaptable to different domains and classification problems. Furthermore, we explore how emotion-bearing patterns can help boost an emotion classification task. The experimented results demonstrate that the extracted patterns are effective in identifying emotions for English, Spanish and French Twitter streams.\n\n[Read Whole Paper](http://dl.acm.org/citation.cfm?doid=2808797.2809419)\n\n## :point_right: Demo\n[MEDA](http://bit.ly/ilmeda)\n\n## :point_right: Demo Screenshot\n![alt text](https://github.com/omarsar/meda/blob/master/public/home.png)\n\n## :point_right: Status\nIn progress :construction:\n\n## :point_right: Contributors\n* [Elvis Saravia](http://elvissaravia.com/) \n* [Carlos Argueta](https://idea.cs.nthu.edu.tw/people.html) \n* [Yi-Shin Chen (Advisor)](http://www.yishin.info/) \n\n## :point_right: To-do sheet\n1. Convert the service into API\n1. Beautification\n1. Integration with other services\n\n## :point_right: How to Contribute?\n* Fork repository\n* Submit Pull Request \n\n## :point_right: Contact\nFor questions please email: :envelope: ellfae@gmail.com","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fomarsar%2Fmeda","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fomarsar%2Fmeda","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fomarsar%2Fmeda/lists"}