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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 – 13 of 13 1 Retweet communities reveal the main sources of hate speech Evkoski, Bojan; Pelicon, Andraž; Mozetič, Igor... In: PLoS One (2022) BASE Show details 2 Slovenian Twitter dataset 2018-2020 1.0 Evkoski, Bojan; Pelicon, Andraž; Mozetič, Igor. - : Jožef Stefan Institute, 2021 BASE Show details 3 Italian YouTube Hate Speech Corpus Cinelli, Matteo; Pelicon, Andraž; Mozetič, Igor. - : Jožef Stefan Institute, 2021 BASE Show details 4 Slovenian Twitter hate speech dataset IMSyPP-sl Kralj Novak, Petra; Mozetič, Igor; Ljubešić, Nikola. - : Jožef Stefan Institute, 2021 BASE Show details 5 English YouTube Hate Speech Corpus Ljubešić, Nikola; Mozetič, Igor; Cinelli, Matteo. - : Jožef Stefan Institute, 2021 BASE Show details 6 Tweets about impact investing Kralj Novak, Petra; de Amicis, Luisa; Mozetič, Igor. - : Jožef Stefan Institute, 2018 BASE Show details 7 Brexit stance annotated tweets Grčar, Miha; Cherepnalkoski, Darko; Mozetič, Igor. - : Jožef Stefan Institute, 2017 BASE Show details 8 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 9 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 10 Emoji Sentiment Ranking 1.0 Kralj Novak, Petra; Smailović, Jasmina; Sluban, Borut. - : Jožef Stefan Institute, 2015 BASE Show details 11 Sentiment of Emojis ... Novak, Petra Kralj; Smailović, Jasmina; Sluban, Borut. - : arXiv, 2015 BASE Show details 12 Sentiment of Emojis Kralj Novak, Petra; Smailović, Jasmina; Sluban, Borut. - : Public Library of Science, 2015 BASE Show details 13 Extraction of Temporal Networks from Term Co-Occurrences in Online Textual Sources Popović, Marko; Štefančić, Hrvoje; Sluban, Borut. - : Public Library of Science, 2014 BASE Show details

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