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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 3 of 3 1 Dataset of European Parliament roll-call votes and Twitter activities MEP 1.0 Cherepnalkoski, Darko; Karpf, Andreas; Mozetič, Igor. - : Jožef Stefan Institute, 2016 BASE Show details 2 Twitter sentiment for 15 European languages Mozetič, Igor; Grčar, Miha; Smailović, Jasmina. - : Jožef Stefan Institute, 2016 Abstract: The dataset contains over 1.6 million tweets (tweet IDs), labeled with sentiment by human annotators. There are 15 Twitter corpora for the corresponding 15 European languages. The data can be used to train and evaluate Twitter sentiment classifiers, to compute annotator agreement, or to study the differences between language usage on Twitter. The data analysis is described in the following papers: I. Mozetič, M. Grčar, J. Smailović. Multilingual Twitter sentiment classification: The role of human annotators, PLoS ONE 11(5): e0155036, doi:10.1371/journal.pone.e0155036, 2016. (http://dx.doi.org/10.1371/journal.pone.0155036) I. Mozetič, L. Torgo, V. Cerqueira, J. Smailović. How to evaluate sentiment classifiers for Twitter time-ordered data?, PLoS ONE 13(3): e0194317, doi:10.1371/journal.pone.0194317, 2018. (https://dx.doi.org/10.1371/journal.pone.0194317) Keyword: annotator self-agreement; inter-annotator agreement; multilingual; sentiment classification; Twitter URL: http://hdl.handle.net/11356/1054 BASE Hide details 3 Multilingual Twitter Sentiment Classification: The Role of Human Annotators ... Mozetic, Igor; Grcar, Miha; Smailovic, Jasmina. - : arXiv, 2016 BASE Show details

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