MTurk-MBTI was collected via Amazon Mechanical Turk platform. It contains, for a total of 347 Amazon Mechanical Turk workers, their self-reported MBTI personality types, and answers to two open-end questions: (1) What is your favourite type of vacations and why? (2) What hobbies do you prefer and why? Each answer to the open-end questions contains minimum 300 characters. The details of the procedure for data collection and the scientific background for choosing those exact two open-end questions can be found in [1]. The annotation reported in [1] was done on instances taken from this full dataset. Dataset can be used only for research non-commercial purposes. If you use this dataset, please reference the following paper: [1] Stajner, S., Yenikent, S. 2021. Why Is MBTI Personality Detection from Texts a Difficult Task? In Proceedings of the 16th conference of the European Chapter of the Association for Computational Linguistics (EACL), pp. 3580-3589. Bibtex reference: @inproceedings{stajner-yenikent-2021-mbti, title = "Why Is {MBTI} Personality Detection from Texts a Difficult Task?", author = "\v{S}tajner, Sanja and Yenikent, Seren", booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume", month = apr, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2021.eacl-main.312", pages = "3580--3589", abstract = "Automatic detection of the four MBTI personality dimensions from texts has recently attracted noticeable attention from the natural language processing and computational linguistic communities. Despite the large collections of Twitter data for training, the best systems rarely even outperform the majority-class baseline. In this paper, we discuss the theoretical reasons for such low results and present the insights from an annotation study that further shed the light on this issue.", }