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1,836 results for “emotion”
Data for manuscript: "Longitudinal Analysis of Sentiment and Emotion in News Media Headlines Using Automated Labelling with Transformer Language Models"
<p>This data set contains automated sentiment and emotionality annotations of 23 million headlines from 47 popular news media outlets popular in the United States. </p> <p>The set of 47 news media outlets analysed (listed in Figure 1 of the main manuscript) was derived from the AllSides organization <a href="https://www.allsides.com/blog/updated-allsides-media-bias-chart-version-11">2019 Media Bias Chart v1.1</a>. The human ratings of outlets’ ideological leanings were also taken from this chart and are listed in Figure 2 of the main manuscript. </p> <p>News articles headlines from the set of outlets analyzed in the manuscript are available in the outlets’ online domains and/or public cache repositories such as The Internet Wayback Machine, Google cache and Common Crawl. Articles headlines were located in articles’ HTML raw data using outlet-specific XPath expressions. </p> <p>The temporal coverage of headlines across news outlets is not uniform. For some media organizations, news articles availability in online domains or Internet cache repositories becomes sparse for earlier years. Furthermore, some news outlets popular in 2019, such as <em>The Huffington Post</em> or <em>Breitbart</em>, did not exist in the early 2000’s. Hence, our data set is sparser in headlines sample size and representativeness for earlier years in the 2000-2019 timeline. Nevertheless, 18 outlets in our data set have chronologically continuous partial or full headline data availability fulfilling our inclusive criteria (see manuscript Methods) since the year 2000. Figure S 1 in the SI reports the number of headlines per outlet and per year in our analysis.</p> <p>In a small percentage of articles, outlet specific XPath expressions might fail to properly capture the content of the headline due to the heterogeneity of HTML elements and CSS styling combinations with which articles text content is arranged in outlets online domains. After manual testing, we determined that the percentage of headlines following in this category is very small. Additionally, our method might miss detecting some articles in the online domains of news outlets. To conclude, in a data analysis of over 23 million headlines, we cannot manually check the correctness of every single data instance and hundred percent accuracy at capturing headlines’ content is elusive due to the small number of difficult to detect boundary cases such as incorrect HTML markup syntax in online domains. Overall however, we are confident that our headlines set is representative of headlines in print news media content for the studied time period and outlets analyzed.</p> <p>The list of compressed files in this data set is listed next:</p> <p>-analysisScripts.rar contains the analysis scripts used in the main manuscript as well as aggregated data of sentiment and emotionality automated annotations of the headlines and human annotations of a subset of headlines sentiment and emotionality used as ground truth. </p> <p>-models.rar contains the Transformer sentiment and emotion annotation models used in the analysis. Namely: </p> <p>Siebert/sentiment-roberta-large-english from https://huggingface.co/siebert/sentiment-roberta-large-english. This model is a fine-tuned checkpoint of <a href="https://huggingface.co/roberta-large">RoBERTa-large</a> (<a href="https://arxiv.org/pdf/1907.11692.pdf">Liu et al. 2019</a>). It enables reliable binary sentiment analysis for various types of English-language text. For each instance, it predicts either positive (1) or negative (0) sentiment. The model was fine-tuned and evaluated on 15 data sets from diverse text sources to enhance generalization across different types of texts (reviews, tweets, etc.). See more information from the original authors at https://huggingface.co/siebert/sentiment-roberta-large-english</p> <p>DistilbertSST2.rar is the default sentiment classification model of the HuggingFace Transformer library https://huggingface.co/ This model is only used to replicate the results of the sentiment analysis with sentiment-roberta-large-english </p> <p>DistilRoberta j-hartmann/emotion-english-distilroberta-base from https://huggingface.co/j-hartmann/emotion-english-distilroberta-base. The model is a fine-tuned checkpoint of <a href="https://huggingface.co/distilroberta-base">DistilRoBERTa-base</a>. The model allows annotation of English text with Ekman's 6 basic emotions, plus a neutral class. The model was trained on 6 diverse datasets. Please refer to the original author at https://huggingface.co/j-hartmann/emotion-english-distilroberta-base for an overview of the data sets used for fine tuning. https://huggingface.co/j-hartmann/emotion-english-distilroberta-base</p> <p>-headlinesDataWithSentimentLabelsAnnotationsFromSentimentRobertaLargeModel.rar URLs of headlines analyzed and the sentiment annotations of the siebert/sentiment-roberta-large-english Transformer model. https://huggingface.co/siebert/sentiment-roberta-large-english</p> <p>-headlinesDataWithSentimentLabelsAnnotationsFromDistilbertSST2.rar URLs of headlines analyzed and the sentiment annotations of the default HuggingFace sentiment analysis model fine-tuned on the SST-2 dataset. https://huggingface.co/</p> <p>-headlinesDataWithEmotionLabelsAnnotationsFromDistilRoberta.rar URLs of headlines analyzed and the emotion categories annotations of the j-hartmann/emotion-english-distilroberta-base Transformer model. https://huggingface.co/j-hartmann/emotion-english-distilroberta-base</p>
Predicting emotion from music videos: exploring the relative contribution of visual and auditory information to affective responses.
<p>The dataset to accompany the paper by Phoebe Chua, Dr. Dimos Makris, Prof. Dorien Herremans, Prof. Gemma Roig, and Prof. Kat Agres on Predicting emotion from music videos: exploring the relative contribution of visual and auditory information on affective responses.</p>
Associations of adverse maternal experiences and diabetes on postnatal maternal depression and child social-emotional outcomes in a South African community cohort
<p>Previous literature has identified associations between diabetes during pregnancy and postnatal maternal depression. Both maternal conditions are associated with adverse consequences on childhood development. Despite an especially high prevalence of diabetes during pregnancy and maternal postnatal depression in low- and middle-income countries, related research predominates in high-income countries. In a South African cohort with or without diabetes, we investigated associations between adverse maternal experiences with postnatal maternal depression and child social-emotional outcomes. South African mother-child dyads were recruited from the Bishop Lavis community in Cape Town. Participants consisted of 82 mother-child dyads (53 women had GDM or type 2 diabetes). At 14–20 months postpartum, maternal self-report questionnaires were administered to assess household socioeconomic status, food insecurity, maternal depressive symptoms (Edinburgh Postnatal Depression Scale (EPDS)), maternal trauma (Life Events Checklist), and child social-emotional development (Brief Infant Toddler Social Emotional Assessment, Ages and Stages Questionnaires: Social-Emotional, Second Edition). Lower educational attainment, lower household income, food insecurity, living without a partner, and having experienced physical assault were each associated with postnatal maternal depressive symptoms and clinical maternal depression (EPDS ≥ 13). Maternal postnatal depression, lower maternal educational attainment, lower household income, household food insecurity, and living in a single-parent household were each associated with child social-emotional problems. Stratified analyses revealed maternal experiences (education, income, food insecurity, trauma) were associated with postnatal maternal depressive symptoms and child social-emotional problems only among dyads with <em>in utero</em> exposure to diabetes. Women with pre-existing diabetes or gestational diabetes in LMIC settings should be screened for health-related social needs to reduce the prevalence of depression and to promote child social-emotional development.</p>
Emotional and work-related factors in the self-assessment of Work Ability
<p>Data on the study "<span>Emotional and work-related factors in the self-assessment of Work Ability"</span></p>
Exploring the Emotional Impact of AI-Driven Simulations in University Education: A Gender Perspective
<p><span>This research evaluates the impact of AI-driven simulations using ChatGPT on university students' emotions, integrating educational psychology and gender studies frameworks. The study investigates how emotions such as interest, irritability, enthusiasm, discomfort, and disgust are affected by AI-enhanced active learning environments. Additionally, it explores gender differences in emotional responses to AI applications in education. Various statistical tests compare emotion scores, including gender comparisons and pre- and post-intervention analyses. The findings aim to enhance understanding of the effectiveness of AI in simulations and its influence on students' emotional experiences, thereby contributing to the design of more effective AI-based educational systems</span></p>
Dataset for "Using Adaptive Immersive Environments to Stimulate Emotional Expression and Connection in Dementia Care: Insights from User Perspectives towards SENSE-GARDEN"
<p>This dataset contains all qualitative interview data recorded from early stage research on an adaptive, immersive, multi-sensory intervention that is being developed for people living with dementia (SENSE-GARDEN). 52 semi-structured interviews were conducted with people living with mild cognitive impairment, informal caregivers, and professional caregivers across Belgium, Norway, Portugal, and Romania. The aim of these interviews was to collect initial user responses towards SENSE-GARDEN. </p> <p>The pdf file "Registration Sheet and Interview Questions" lists the questions that were asked during the interviews. The excel file "Interview Data with Thematic Analysis" contains all raw interview data with ideas, notes, and codes made during thematic analysis. The first three sheets in the file correspond to the user type (Person with mild cognitive impairment/Informal Caregiver/Professional Caregiver). The fourth sheet, "Overall themes", gives an overview of each theme, subtheme, and relevant quotes belonging to these themes. </p> <p>This research was conducted as part of a larger project. The SENSE-GARDEN project (AAL/Call2016/054-b/2017, implementation period June 2017 - May 2020) is funded by AAL Programme, co-funded by the European Commission and National Funding Authorities of Norway, Belgium, Romania, and Portugal. </p>
Emotions of Social Eating for People with Selective Eating Habits (ESE1)
<p><strong><em>Emotions of Social Eating for </em></strong><strong><em>People with Selective Eating Habits (ESE1) data set</em></strong></p> <p> </p>
Positive and Negative Affect Schedule (PANAS): psychometric properties for the online version in a clinical sample with emotional disorders
<p>This dataset contains sociodemographic and clinical data about 595 patients with emotional disorders that participated in a study with the objective of examining the psychometric properties of the online version of the Positive and Negative Affect Schedule (PANAS) in Spanish clinical sample.</p> <p> </p> <p> </p>
A Canadian French Emotional Speech Dataset
<p>The Canadian French Emotional (CaFE) speech dataset contains six different sentences, pronounced by six male and six female actors, in six basic emotions plus one neutral emotion. The six basic emotions are acted in two different intensities: mild ("Faible") and strong ("Fort").</p> <p>This dataset is freely available under a Creative Commons license (CC BY-NC-SA 4.0).</p> <p>The dataset was digitally recorded at a high-resolution (192 kHz sampling rate, 24 bits per sample).</p> <p>A filtered and downsampled version at a lower resolution (48 kHz sampling rate, 16 bits per sample) is also available.</p> <p>This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.</p> <p>To view a copy of this license, visit <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons</a></p>
Bodily maps of emotions are culturally universal
<p>Data from the manuscript "Bodily maps of emotions are culturally universal".</p> <p>Pheno.csv contains data for each subject as well as the amount of time in ms to paint each emotion and the amount of pixels painted.</p> <p>The folder "bodymaps" contains the 3954 bodymaps in matlab format.</p>
BabyRobot Emotion Database (BRED)
<p>BabyRobot Emotion Database (BRED)</p> <p>This dataset includes extracted data from videos of children performing emotions. The dataset has a total of 215 samples and includes:</p> <ul> <li>Skeletons extracted by <a href="https://github.com/CMU-Perceptual-Computing-Lab/openpose">OpenPose</a>.</li> <li>Facial landmarks extracted by the [OpenFace toolkit](<a href="https://github.com/TadasBaltrusaitis/OpenFace">OpenFace toolkit</a>)</li> <li>Features extracted by a ResNet 50 network pretrained in the <a href="http://mohammadmahoor.com/affectnet/">AffectNet</a> database. Features are in <a href="https://pytorch.org">PyTorch</a> format.</li> <li>Annotations by three different annotators. The annotations are hierarchical and apart from the ground truth emotion, denote if the child used its body and/or face for performing the emotion.</li> <li><strong>Note</strong> that there is an error in the annotations.csv file. Replace "spontaneous" in the path with "game" and "acted" with "pre-game" to get the correct paths.</li> </ul> <p>We also provide the code on <a href="https://github.com/filby89/body-face-emotion-recognition">Github</a>. The accompanied paper can be found on <a href="https://arxiv.org/abs/1901.01805">arXiv</a>.</p>
Dataset of the article: "The social dimension of equine welfare: social contact positively affects the emotional state of stalled horses"
<p>Here are the data used in the analyses of the manuscript "The social dimension of equine welfare: social contact positively affects the emotional state of stalled horses.".<br>Experiment 1: Observations in the individual stall<br>Experiment 2: Observations during horse grooming<br>Experiment 3: Judgement bias test</p>
Data Files for the study "Exploring risk-taking behaviour as a function of cognitive flexibility and emotional intelligence"
<p><span>Risk-taking behaviour refers to how one decides to act with the possibility of negative outcomes which is often pursued in the hope of achieving a lucrative reward, and it can happen in various circumstances, which makes it important to study and understand the key factors behind this cognitive process. The present study focused on understanding these dynamics with emotional intelligence and cognitive flexibility. The present study incorporated a healthy sample (<em>n=</em>121) whose emotional intelligence was assessed with MSREIS-R, risk-taking with GDT and cognitive flexibility with WCST. The findings suggested that cognitive flexibility and sub-scale of emotional intelligence did impact risk-taking behaviour, while no moderating effect was found between them. However, the sub-scales of emotional intelligence did predict cognitive flexibility hinting at their interactive relationship which does not erase the possibility of them not affecting risk-taking behaviour completely.<span> </span></span></p> <p>Emotional intelligence questionnaire and manuals, an Excel file consisting of Emotional Intelligence Score, SPSS file, Software file (Inquisit Program file), the link to the task script and other important links, Game of dice task(raw and summary data), Wisconsin Card Sorting Test (raw and summary data).</p>
Paradise fish (Macropodus opercularis) as novel translational model for emotional and cognitive function - RAW DATA
<p>The data of the study under the title "Paradise fish (Macropodus opercularis) as novel translational model for emotional and cognitive function". The structure of the data follows the structure of the results section of the paper. Data sheets are labeled according to figure numbers of the papers.</p>
Data Appendix for Lack, P., "Using Word Analysis to Track the Evolution of Emotional Well-being in Nineteenth-Century Industrializing Britain", Historical Methods (forthcoming)
<p>This file contains the data associated with the publication Lack, P., "Using Word Analysis to Track the Evolution of Emotional Well-being in Nineteenth-Century Industrializing Britain", <em>Historical Methods</em> (forthcoming). It quantifies the trend in emotional well-being expressed in a corpus of British pamphlets published between 1800 and 1900. The first page of the excel document presents this key data on the trend in emotional well-being. Sheet 1A presents summary statistics on the trend in emotional well-being and its correlation with GDP per capita and real wages. </p>
emoUERJ: an emotional speech database in Portuguese
<p><strong>GOAL</strong></p> <p>Since language is a key issue in speech emotion recognition (SER) and there are few databases in Portuguese, this database was developed at the State University of Rio de Janeiro aiming to the development of specific SER models for this language.</p> <p> </p> <p><strong>DATABASE DESIGN</strong></p> <p>Ten sentences were made available to eight actors, equally divided between genders, and they were free to choose the phrases for record audios in four emotions target: happiness, anger, sadness or neutral. The following phrases were used:</p> <ul> <li>Não importa quem está certo. (It doesn't matter who is right.)</li> <li>Você perde tempo demais com a Internet. (You waste too much time on the Internet.)</li> <li>A garrafa está na geladeria. (The bottle is in the fridge.)</li> <li>Eu estou me sentindo doente hoje. (I'm feeling sick today)</li> <li>Eu estou um pouco atrasado. (I'm a little late)</li> <li>Nos fins de semana, eu sempre ia para a casa dele(a). (On weekends, I always used to go to his/her house)</li> <li>De quem são essas malas que estão debaixo da mesa? (Whose bags are under the table?)</li> <li>Ele volta na quarta-feira. (He comes back on wednesday)</li> <li>Já chega! Eu vou tomar um banho e ir para a cama. (Enough! I'm going to take a shower and go to bed)</li> <li>Você poderia arrumar a mesa, por favor? (Could you set the table, please?)</li> </ul> <p>The result of this process was 377 audios distributed as follows</p> <ul> <li>happiness: 91</li> <li>anger: 94</li> <li>sadness: 100</li> <li>neutral: 92</li> </ul> <p><strong>FILE IDENTIFICATION</strong></p> <p>Each database file corresponds to a phrase recorded by an actor expressing one of the four emotions and was named as follows:</p> <ul> <li>Position 1: actor's gender ('m' for man or 'w' for woman)</li> <li>Positions 2 and 3: actor's id (from 01 to 04)</li> <li>Position 4: emotion (h: happiness, a: anger, s: sadness, n: neutral)</li> <li>Positions 5 and 6: recording identification</li> </ul> <p>For example, the file 'w04a11' was the eleventh audio recorded by actress 04 interpreting the anger emotion.</p>
Thorsten-Voice Dataset 2021.06 emotional
<p>Thorsten-Voice (Thorsten-21.06-emotional) is an emotional dataset recorded by Thorsten Müller, audio optimized by Dominik Kreutz and licenced under CC0 to provide it for anybody without any financial or licence struggle.</p> <blockquote> <p><strong>"I contribute my personal voice as a person believing in a world where all people are equal. No matter of gender, sexual orientation, religion, skin color and geocoordinates of birth location. A global world where everybody is warmly welcome on any place on this planet and open and free knowledge and education is available to everyone."</strong> (Thorsten Müller)</p> </blockquote> <p>Dataset contains 2.400 recordings.</p> <ul> <li>300 sentences * 8 emotions = 2.400 recordings</li> <li>recorded by Thorsten Müller</li> <li>audio optimized by Dominik Kreutz</li> <li>mono</li> <li>samplerate 22.050Hz</li> <li>normalized to -24dB</li> <li>no silence at beginning/ending</li> <li>sentence length: 59 - 148 chars</li> </ul> <p><strong>Here's the audio length by emotion:</strong></p> <ul> <li>Neutral 🙂19 min.</li> <li>Disgusted 🤢23 min.</li> <li>Angry 😠20 min.</li> <li>Amused 😀18 min.</li> <li>Surprised 😲18 min.</li> <li>Sleepy 😔30 min.</li> <li>Drunk (<em>i was "not" drunk while recording, just pronouncing it this way!</em>) 😵25 min.</li> <li>Whispering 🤫22 min.</li> </ul> <p>For more details see my <a href="https://github.com/thorstenMueller/Thorsten-Voice">Github page</a> or <a href="https://www.Thorsten-Voice.de">Thorsten-Voice</a> project website.</p>
Emotional and Cognitive Changes Surrounding Online Depression Identity Claims
<p>The repository includes data files containing anonymized user IDs, timestamps, identity claim time, LIWC variables, post vs. comment (boolean), and mental health vs. other subreddit (boolean) for our paper <em>Emotional and Cognitive Changes Surrounding Online Depression Identity Claims </em>to replicate LIWC analysis. These files are named ic_liwc.csv (for users with identity claims) and control_liwc.csv (for users without identity claims). Because the identity claims themselves are excluded from these files but metadata about them is required to split users into groups, we also provide a file for doing so, ic_properties.csv.</p> <p>We have also included files with n-gram counts to reproduce our n-gram analysis, and files with the patterns used to collect the data.</p> <p>The Reddit posts themselves will not be made widely available, following the lead of Cohan et al. (the paper with the data collection process we follow) who only release raw text data to researchers upon request.</p>
Using social media and personality traits to assess software developers' emotions
<p>Companion DATA</p> <p>Title:<br> Using social media and personality traits to assess software developers' emotions</p> <p>Authors: <br> Leo Moreira Silva<br> Marília Gurgel Castro<br> Miriam Bernardino Silva<br> Milena Nestor Santos<br> Uirá Kulesza<br> Margarida Lima<br> Henrique Madeira</p> <p>Journal:<br> PeerJ Computer Science</p> <p>Github:<br> <a href="https://github.com/leosilva/peerj_computer_science_2022">https://github.com/leosilva/peerj_computer_science_2022</a></p> <p>------------------------------------------------------------<br> The folders contain:</p> <p>Experiment_Protocol.pdf: document that present the protocol regarding recruitment protocol, data collection of public posts from Twitter, criteria for manual analysis, and the assessment of Big Five factors from participants and psychologists. English version.</p> <p><br> /analysis<br> analyzed_tweets_by_psychologists.csv: file containing the manual analysis done by psychologists<br> analyzed_tweets_by_participants.csv: file containing the manual analysis done by participants<br> analyzed_tweets_by_psychologists_solved_divergencies.csv: file containing the manual analysis done by psychologists over 51 divergent tweets' classifications</p> <p><br> /dataset<br> alldata.json: contains the dataset used in the paper</p> <p>/ethics_committee<br> committee_response_english_version.pdf: contains the acceptance response of Research Ethics and Deontology Committee of the Faculty of Psychology and Educational Sciences of the University of Coimbra. English version.<br> committee_response_original_portuguese_version: contains the acceptance response of Research Ethics and Deontology Committee of the Faculty of Psychology and Educational Sciences of the University of Coimbra. Portuguese version.<br> committee_submission_form_english_version.pdf: the project submitted to the committee. English version.<br> committee_submission_form_original_portuguese_version.pdf: the project submitted to the committee. Portuguese version.<br> consent_form_english_version.pdf: declaration of free and informed consent fulfilled by participants. English version.<br> consent_form_original_portuguese_version.pdf: declaration of free and informed consent fulfilled by participants. Portuguese version.<br> data_protection_declaration_english_version.pdf: personal data and privacy declaration, according to European Union General Data Protection Regulation. English version.<br> data_protection_declaration_original_portuguese_version.pdf: personal data and privacy declaration, according to European Union General Data Protection Regulation. Portuguese version.</p> <p>/notebooks<br> General - Charts.ipynb: notebook file containing all charts produced in the study, including those in the paper<br> Statistics - Lexicons and Ensembles.ipynb: notebook file with the statistics for the five lexicons and ensembles used in the study<br> Statistics - Linear Regression.ipynb: notebook file with the multiple linear regression results<br> Statistics - Polynomial Regression.ipynb: notebook file with the polynomial regression results<br> Statistics - Psychologists versus Participants.ipynb: notebook file with the statistics between the psychologists and participants manual analysis<br> Statistics - Working x Non-working.ipynb: notebook file containing the statistical analysis for the tweets posted during work period and those posted outside of working period</p> <p><br> /surveys<br> Demographic_Survey_english_version.pdf: survey inviting participants to enroll in the study. We collect demographic data and participants' authorization to access their public Tweet posts. English version.<br> Demographic_Survey_portuguese_version.pdf: survey inviting participants to enroll in the study. We collect demographic data and participants' authorization to access their public Tweet posts. Portuguese version.<br> Demographic_Survey_answers.xlsx: participants' demographic survey answers<br> ibf_pt_br.doc: the Portuguese version of the Big Five Inventory (BFI) instrument to infer participants' Big Five polarity traits.<br> ibf_en.doc: translation in English of the Portuguese version of the Big Five Inventory (BFI) instrument to infer participants' Big Five polarity traits.<br> ibf_answers.xlsx: participantes' and psychologists' answers for BFI</p> <p><br> ------------------------------------------------------------</p> <p>We have removed from dataset any sensible data to protect participants' privacy and anonymity.<br> We have removed from demographic survey answers any sensible data to protect participants' privacy and anonymity.</p>
Data and code accompanying "'Safe spaces' and community building for climate scientists, exploring emotions through a case study", Haddaway and Duggan 2023
<p>Data and code accompanying "‘Safe spaces’ and community building for climate scientists, exploring emotions through a case study", Haddaway and Duggan 2023</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.