HTTPS SSH

Motivation

Relevancer aims at identifying relevant content in social media streams. Text mining is the main approach.

Contributors

Ali Hürriyetoglu (@hurrial), Mustafa Erkan Başar (@me_basar), Nelleke Oostdijk, Antal van den Bosch (@antalvdb), Aslıhan Arslan (@miniminiibirkus), Uğur Özcan (@uozcan12), Jurjen Wagemaker (@jurjenwagemaker), Ghiath Ghanem, and Ron Bortman.

Publications

Hürriyetoglu, A., Gudehus C., Oostdijk, N. H. J., & van den Bosch, A. P. J. (2016a). Relevancer: Finding and Labeling Relevant Information in Tweet Collections. In International Conference on Social Informatics (pp. 1-15). Springer International Publishing. LINK: http://link.springer.com/chapter/10.1007/978-3-319-47874-6_15

Hürriyetoglu, A., Wagemaker J., Oostdijk, N. H. J., & van den Bosch, A. P. J. (2016b). Analysing the Role of Key Term Inflections in Knowledge Discovery on Twitter. In Proceedings of the 2st International Workshop on Knowledge Discovery on the WEB. Cagliari, Italy, September 8-10, 2016.

Hürriyetoglu, A., van den Bosch, A., & Oostdijk, N. (2016c). Using Relevancer to Detect Relevant Tweets: The Nepal Earthquake Case. Working notes of FIRE 2016 - Forum for Information Retrieval Evaluation, Kolkata, India, December 7-10, 2016. Url: http://ceur-ws.org/Vol-1737/T2-6.pdf

Demo

http://relevancer.science.ru.nl/

API: https://bitbucket.org/hurrial/relevancerapi

Machine learning: https://bitbucket.org/hurrial/relevancerml

License

Licensed under GPLv3 (See http://www.gnu.org/licenses/gpl-3.0.html)

Acknowledgements

This project was supported by Floodtags (@FloodTags) and the Dutch national programme COMMIT (@COMMIT_nl as part of the Infiniti project, Work Package ADNEXT (@Adnext_Commit).