A Twitter Corpus and Benchmark Resources for German Sentiment Analysis

no code implementations • WS 2017 • Mark Cieliebak, Jan Milan Deriu, Dominic Egger, Fatih Uzdilli

We use this new corpus and two existing corpora to provide state-of-the-art benchmarks for sentiment analysis in German: we implemented a CNN (based on the winning system of SemEval-2016) and a feature-based SVM and compare their performance on all three corpora.

Named Entity Recognition (NER) Sentiment Analysis +1

SwissCheese at SemEval-2016 Task 4: Sentiment Classification Using an Ensemble of Convolutional Neural Networks with Distant Supervision

no code implementations • SEMEVAL 2016 • Jan Deriu, Maurice Gonzenbach, Fatih Uzdilli, Aurelien Lucchi, Valeria De Luca, Martin Jaggi

General Classification Sentence Embedding +3

Swiss-Chocolate: Combining Flipout Regularization and Random Forests with Artificially Built Subsystems to Boost Text-Classification for Sentiment

no code implementations • SEMEVAL 2015 • Fatih Uzdilli, Martin Jaggi, Dominic Egger, Pascal Julmy, Leon Derczynski, Mark Cieliebak

General Classification Sentiment Analysis +2

JOINT\_FORCES: Unite Competing Sentiment Classifiers with Random Forest

no code implementations • SEMEVAL 2014 • Oliver D{\"u}rr, Fatih Uzdilli, Mark Cieliebak

Sentiment Analysis

Swiss-Chocolate: Sentiment Detection using Sparse SVMs and Part-Of-Speech n-Grams

no code implementations • SEMEVAL 2014 • Martin Jaggi, Fatih Uzdilli, Mark Cieliebak

Sentiment Analysis

Meta-Classifiers Easily Improve Commercial Sentiment Detection Tools

no code implementations • LREC 2014 • Mark Cieliebak, Oliver D{\"u}rr, Fatih Uzdilli

In this paper, we analyze the quality of several commercial tools for sentiment detection.

BIG-bench Machine Learning Opinion Mining +1