The RANLP-Emotions-Twitter dataset contains 210 English tweets annotated by six trained annotators for Ekman's basic emotions plus the neutral class. The details of the annotation procedure and various analyses can be found in [1]. Dataset can be used only for research non-commercial purposes. If you use this dataset, please reference the following paper: [1] Štajner, S. 2021. Exploring Reliability of Gold Labels for Emotion Detection in Twitter. In Proceedings of the 13th international conference on Recent Advances in Natural Language Processing (RANLP), pp. 1350-1359. Bibtex reference: @inproceedings{stajner-2021-ranlp-emotions, title = "Exploring Reliability of Gold Labels for Emotion Detection in Twitter", author = "\v{S}tajner, Sanja", booktitle = "Proceedings of the 13th international conference on Recent Advances in Natural Language Processing (RANLP)", month = sep, year = "2021", address = "Online", pages = "1350--1359", abstract = "Emotion detection from social media posts has attracted noticeable attention from natural language processing (NLP) community in recent years. The ways for obtaining gold labels for training and testing of the systems for automatic emotion detection differ significantly from one study to another, and pose the question of reliability of gold labels and obtained classification results. This study systematically explores several ways for obtaining gold labels for Ekman's emotion model on Twitter data and the influence of the chosen strategy on the manual classification results."}

{"references": ["\u0160tajner, S. 2021. Exploring Reliability of Gold Labels for Emotion Detection in Twitter. In Proceedings of the 13th international conference on Recent Advances in Natural Language Processing (RANLP), pp. 1350-1359."]}