Background: In health care research, patient-reported opinions are a critical element of personalized medicine and contribute to optimal health care delivery. The importance of integrating natural language processing (NLP) methods to extract patient-reported opinions has been gradually acknowledged over the past years. One form of NLP is sentiment analysis, which extracts and analyses information by detecting feelings (thoughts, emotions, attitudes, etc) behind words.
View Article and Find Full Text PDFStories do not fossilize. Thus, exploring tales shared during prehistory, the longest part of human history inevitably becomes speculative. Nevertheless, various attempts have been made to find a more scientifically valid way into our deep human past of storytelling.
View Article and Find Full Text PDFIf the words of natural human language possess a universal positivity bias, as assumed by Boucher and Osgood's (1969) famous Pollyanna hypothesis and computationally confirmed for large text corpora in several languages (Dodds et al., 2015), then children and youth literature (CYL) should also show a Pollyanna effect. Here we tested this prediction applying an unsupervised vector space model-based sentiment analysis tool called (Jacobs, 2019) to two CYL corpora, one in English (372 books) and one in German (500 books).
View Article and Find Full Text PDFThe end of deep reading is a commonplace in public debates, whenever societies talk about youth, books, and the digital age. In contrast to this, we show for the first time and in detail, how intensively young readers write and comment literary texts at an unprecedented scale. We present several analyses of how fiction is transmitted through the social reading platform Wattpad, one of the largest platforms for user-generated stories, including novels, fanfiction, humour, classics, and poetry.
View Article and Find Full Text PDF