Issue Date | Title | Involved Person(s) |
2017 | A Twitter corpus and benchmark resources for german sentiment analysis | Cieliebak, Mark; Deriu, Jan Milan; Egger, Dominic; Uzdilli, Fatih |
2016 | Adverse drug reaction detection using an adapted sentiment classifier | Uzdilli, Fatih; Cieliebak, Mark; Egger, Dominic |
2016 | Twitter can help to find adverse drug reactions | Cieliebak, Mark; Egger, Dominic; Uzdilli, Fatih |
2015 | Swiss-Chocolate : combining flipout regularization and random forests with artificially built subsystems to boost text-classification for sentiment | Uzdilli, Fatih; Jaggi, Martin; Egger, Dominic; Julmy, Pascal; Derczynski, Leon, et al |
2015 | PANOPTES : automated article segmentation of newspaper pages for "Real Time Print Media Monitoring“ | Arnold, Marek; Cieliebak, Mark; Stadelmann, Thilo; Stampfli, Jan; Uzdilli, Fatih |
2015 | Back to the Future : Time-Travelling-Debugger als Alternative zu klassischen Debuggern | Schutzbach, Daniel; Uzdilli, Fatih; Cieliebak, Mark |
2014 | JOINT_FORCES : unite competing sentiment classifiers with random forest | Dürr, Oliver; Uzdilli, Fatih; Cieliebak, Mark |
2014 | Swiss-chocolate : sentiment detection using sparse SVMs and part-of-speech n-grams | Jaggi, Martin; Uzdilli, Fatih; Cieliebak, Mark |
2014 | Meta-classifiers easily improve commercial sentiment detection tools | Cieliebak, Mark; Dürr, Oliver; Uzdilli, Fatih |
2013 | Potential and limitations of commercial sentiment detection tools | Cieliebak, Mark; Dürr, Oliver; Uzdilli, Fatih |