https://github.com/omarsar/meda
:smile: :worried: :joy: An emotion classification web service :smile: :worried: :joy:
https://github.com/omarsar/meda
classification-task emotion-analysis emotion-analytics emotion-api graph-theory machine-learning sentiment-analysis
Last synced: 7 months ago
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:smile: :worried: :joy: An emotion classification web service :smile: :worried: :joy:
- Host: GitHub
- URL: https://github.com/omarsar/meda
- Owner: omarsar
- Created: 2016-05-24T05:29:25.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2016-06-12T04:20:08.000Z (over 9 years ago)
- Last Synced: 2025-01-23T06:22:27.343Z (9 months ago)
- Topics: classification-task, emotion-analysis, emotion-analytics, emotion-api, graph-theory, machine-learning, sentiment-analysis
- Language: HTML
- Homepage: http://bit.ly/ilmeda
- Size: 43 KB
- Stars: 5
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## :point_right: An Multilingual Emotion Classification Web Service
This 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/)
# :point_right: Abstract
Traditional 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.
[Read Whole Paper](http://dl.acm.org/citation.cfm?doid=2808797.2809419)
## :point_right: Demo
[MEDA](http://bit.ly/ilmeda)
## :point_right: Demo Screenshot
## :point_right: Status
In progress :construction:
## :point_right: Contributors
* [Elvis Saravia](http://elvissaravia.com/)
* [Carlos Argueta](https://idea.cs.nthu.edu.tw/people.html)
* [Yi-Shin Chen (Advisor)](http://www.yishin.info/)
## :point_right: To-do sheet
1. Convert the service into API
1. Beautification
1. Integration with other services
## :point_right: How to Contribute?
* Fork repository
* Submit Pull Request
## :point_right: Contact
For questions please email: :envelope: ellfae@gmail.com