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This dataset features training and test tweets as well as insights into the classifier model related to the paper:Kunneman, F.A., Liebrecht, C.C., Mulken, M.J.P. van & Bosch, A.P.J. van den (2015). Signaling sarcasm: From hyperbole to hashtag. Information Processing & Management, 51 (4), 500-509. doi: 10.1016/j.ipm.2014.07.006.The tweets used to train and test a model for detecting sarcasm in our study are shared as tweet ids and can be used to query the complete tweets from Twitter. We also include an analysis of the most indicative indicators of sarcasm in tweets.The collection period (June 1st until June 30th 2013) relates to the period in which the tweets have been queried from TwiNL (https://twinl.surfsara.nl/), which deviates from the subject period (December 16th 2010 until February 1st 2013). Date: Collection period: start=2013-06-01; end=2013-06-30;

In the context of the research programme ‘Idiomatic Second Language Acquisition’ (for more information see http://isla.ruhosting.nl), we collected normative data of 374 Dutch idiomatic expressions by 390 native speakers. In an online test, we asked participants to judge various dimensions of idiomatic expressions on a five-point scale: Frequency, Usage, Familiarity, Imageability, and Transparency. In addition, we objectively assessed their knowledge of idiom meaning by means of a multiple choice question (Idiom knowledge recognition). The dataset contains the aggregated results per expression for the 5 subjective dimensions (Frequency, Usage, Familiarity, Imageability, and Transparency) and the objective Idiom knowledge recognition (proportion correct).This work is part of the research program Free Competition in the Humanities with project number 23000349 NWO ISLA FdL, which is financed by the Netherlands Organisation for Scientific Research (NWO).

This dataset is an ATLAS.ti copy bundle that contains the analysis of 86 articles that appeared between March 2011 and March 2013 in the Dutch quality newspaper NRC Handelsblad in the weekly article series 'the last word' [Dutch: 'het laatste woord'] that were written by NRC editor Gijsbert van Es. Newspaper texts have been retrieved from LexisNexis (http://academic.lexisnexis.nl/). These articles describe the experience of the last phase of life of people who were confronted with approaching death due to cancer or other life-threatening diseases, or due to old age and age-related health losses. The analysis focuses on the meanings concerning death and dying that were expressed by these people in their last phase of life. The data-set was analysed with ATLAS.ti and contains a codebook. In the memo manager a memo is included that provides information concerning the analysed data. Culturally embedded meanings concerning death and dying have been interpreted as 'death-related cultural affordances': possibilities for perception and action in the face of death that are offered by the cultural environment. These have been grouped into three different ‘cultural niches’ (sets of mutually supporting cultural affordances) that are grounded in different mechanisms for determining meaning: a canonical niche (grounding meaning in established (religious) authority and tradition), a utilitarian niche (grounding meaning in rationality and utilitarian function) and an expressive niche (grounding meaning in authentic (and often aesthetic) self-expression. Interviews are in Dutch; Codes, analysis and metadata are in English.

Spiritualiteit en maatschappelijke vernieuwing - negen initiatieven van maatschappelijke vernieuwing rond eten, zorg en geld in Nederland.Vanuit hun persoonlijke toe-eigening van een gegeven maatschappelijk probleem en hun eigen creatieve vermogen om de dingen anders aan te pakken, dragen vernieuwers in Nederland bij aan een andere inrichting van de samenleving rond de thema’s eten, zorg en geld. Wie zijn bij deze initiatieven betrokken, wat doen zij precies, hoe doen zij dat en waartoe leidt dit?Deze vragen worden per initiatief in detail onderzocht. Deze initiatieven zijn: Uit Je Eigen Stad, Hotspot Hutspot, Thuis Afgehaald, Zorgcoöperatie Hoogeloon, Unal zorg, Eigen Kracht Centrale, Stro, Qoin en Money Money Money. De data van dit project bestaat uit transcripties van afgenomen interviews.Dit is het interview over zorg met Aysel Disbudak van het initiatief Unal Zorg.

This dataset features the training models, emotion classifications and emotion patterns before and after events, related to the paper:F. Kunneman, M. van Mulken and A. Van den Bosch, Anticipointment detection in event tweets (under review)Abstract of the study:We developed a system to detect positive expectation, disappointment, and satisfaction in tweets that refer to events automatically discovered in the Twitter stream. The emotional content shared on Twitter when referring to public events can provide insights into the presumed and experienced quality of the event. We expected to find a connection between positive expectation and disappointment, a succession that is sometimes referred to as anticipointment. The application of computational approaches makes it possible to detect the presence and strength of this hypothetical relation for a large number of events. We extracted events from a longitudinal data set of Dutch Twitter posts, and modeled classifiers to recognize emotion in the tweets related to those events by means of hashtag-labeled training data. After classifying all tweets before and after the events in our data set, we summarized the collective emotions by calculating the percentage of tweets classified with an emotion as well as ranking tweets based on the classifier confidence score for an emotion and selecting the 90th percentile. Only a weak correlation of around 0.2 was found between positive expectation and disappointment, while a higher correlation of 0.6 was found between positiveexpectation and satisfaction. The most anticipointing events were events with a clear loss, such as a canceled event or when the favored sports team had lost. We conclude that senders of Twitter posts might be more inclined to share satisfaction than disappointment after a much anticipated event.Subject period: January 1st 2011 until October 31st 2015 Files not yet migrated to Data Station. Files for this dataset can be found at https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:72574. Date: start=2015-11-01; end=2016-02-28 (data collection)

Nevumba talks about her life, about the relationship of the Akie with the Maasai asked by the researcher. Nevumba uses Kisankare words.