If 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). Pitching our analysis at the sentence level, and assessing semantic as well as lexico-grammatical information, both corpora show the Pollyanna effect and thus add further evidence to the universality hypothesis. The results of our multivariate sentiment analyses provide interesting testable predictions for future scientific studies of literature.
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http://dx.doi.org/10.3389/fpsyg.2020.574746 | DOI Listing |
Objectives: To investigate and compare the educational value of the most popular testimonials between TikTok (TT) and YouTube (YT), and to classify the emotional response of viewers through sentiment analysis of video comments on both platforms involving orthodontic patients who will undergo orthognathic surgery.
Materials And Methods: Two distinct social media searches were conducted using specific search phrases on TT and YT. For each search phrase, 30 videos were gathered from each platform, and details such as number of views, likes, and comments were recorded for each video.
Nicotine Tob Res
January 2025
Faculty of Public Health & Policy, London School of Hygiene and Tropical Medicine, Keppel St, London WC1E 7HT, United Kingdom.
Data Brief
February 2025
Symbiosis Institute of Business Management, Symbiosis International (Deemed University), Bengaluru 560100, India.
The CoWIN Twitter Dataset offers a wide-ranging collection of public opinions on India's COVID-19 vaccination platform CoWIN. The raw dataset has 635,000 tweets that mention "cowin," collected over the period of January to December 2021. The dataset was extracted by employing the Twitter Academic API.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Division of Surgery & Interventional Science, Faculty of Medical Sciences, University College London, London, United Kingdom.
Background: The literature is equivocal as to whether the predicted negative mental health impact of the COVID-19 pandemic came to fruition. Some quantitative studies report increased emotional problems and depression; others report improved mental health and well-being. Qualitative explorations reveal heterogeneity, with themes ranging from feelings of loss to growth and development.
View Article and Find Full Text PDFSci Rep
January 2025
Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing, 100081, China.
Aspect Category Sentiment Analysis (ACSA) is a fine-grained sentiment analysis task aimed at predicting the sentiment polarity associated with aspect categories within a sentence.Most existing ACSA methods are based on a given aspect category to locate sentiment words related to it. When irrelevant sentiment words have semantic meaning for the given aspect category, it may cause the problem that sentiment words cannot be matched with aspect categories.
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