Potential and Limitations of Commercial Sentiment Detection Tools. - Mark Cieliebak; Oliver Dürr; Fatih Uzdilli (ESSEM@AI*IA, 2013)

Meta-Classifiers Easily Improve Commercial Sentiment Detection Tools. - Mark Cieliebak; Oliver Dürr; Fatih Uzdilli (LREC, 2014)

JOINT_FORCES: Unite Competing Sentiment Classifiers with Random Forest. - Oliver Dürr; Fatih Uzdilli; Mark Cieliebak (SemEval@COLING, 2014)

Swiss-Chocolate: Sentiment Detection using Sparse SVMs and Part-Of-Speech n-Grams. - Martin Jaggi; Fatih Uzdilli; Mark Cieliebak (SemEval@COLING, 2014)

Swiss-Chocolate: Combining Flipout Regularization and Random Forests with Artificially Built Subsystems to Boost Text-Classification for Sentiment. - Fatih Uzdilli; Martin Jaggi; Dominic Egger; Pascal Julmy; Leon Derczynski; Mark Cieliebak (SemEval@NAACL-HLT, 2015)

SwissCheese at SemEval-2016 Task 4: Sentiment Classification Using an Ensemble of Convolutional Neural Networks with Distant Supervision. - Jan Deriu; Maurice Gonzenbach; Fatih Uzdilli; Aurélien Lucchi; Valeria De Luca; Martin Jaggi (SemEval@NAACL-HLT, 2016)

Twitter can Help to Find Adverse Drug Reactions. - Mark Cieliebak; Dominic Egger; Fatih Uzdilli (ERCIM News, 2016)

A Twitter Corpus and Benchmark Resources for German Sentiment Analysis. - Mark Cieliebak; Jan Deriu; Dominic Egger; Fatih Uzdilli (SocialNLP@EACL, 2017)