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23 results for “emotion analysis”
Emotion Category and Face Perception Task Optimized for Multivariate Pattern Analysis
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Experimental datasets for sentiment analysis and emotion mining - Emotion Mining Toolkit (EMTk)
<p><strong>Description</strong></p> <p>Datasets for sentiment analysis and emotion mining, distributed with the Emotion Mining Toolkit (EMTk) Docker container (see <a href="https://collab-uniba.github.io/EMTk">https://collab-uniba.github.io/EMTk</a> for more):</p> <ul> <li>Stack Overflow - A couple of gold standards of 4,000+ posts, manually annotated for mining both emotions and polarity.</li> <li>Jira - A gold standard of ~4,000 issues, manually annotated for emotions.</li> </ul> <p><strong>Citation</strong></p> <p>Please, see the references below for the papers to cite. Do not cite this Zenodo upload directly.</p>
VnEmoLex: A Vietnamese emotion lexicon for sentiment intensity analysis
<p>VnEmoLex is the moderate-sized data set annotated for eight basic emotions: joy, sadness, anger, fear, trust, disgust, surprise and anticipation for Vietnamese. It is built on the NRC Word-Emotion Association Lexicon (EmoLex)<sup>1 </sup>and the Viet Wordnet<sup>2</sup> . VnEmoLex has total 12,795 words of which 4431 words are from the EmoLex dictionary, 8364 words are taken from the Viet Wordnet.</p> <p><sup>1 </sup>http://saifmohammad.com/WebPages/NRC-Emotion-Lexicon.htm</p> <p><sup>2 </sup>http://http://viet.wordnet.vn/wnms/</p>
Use of artificial intelligence techniques for the recognition of human emotions: a bibliometric analysis
<p>Human emotion recognition with AI uses physiological, audiovisual, and linguistic signals. Despite its importance and great progress in emotion recognition, several challenges remain in generalization and evaluation through standards and shared data, as well as other research gaps. Therefore, the objective is to analyze the scientific production on the use of artificial intelligence techniques for the recognition of human emotions. This study uses bibliometric analysis following the guidelines of the PRISMA-2020 statement for literature reviews. Based on the results of the bibliometrics on the use of artificial intelligence techniques in for the recognition of human emotions, significant conclusions are obtained that improve the understanding of the current panorama in this field of research. A growing interest in the subject is observed during the years 2023, 2022, 2021 and 2020, which demonstrates the relevance and potential of artificial intelligence in the recognition of human emotions. A cubic polynomial growth in the number of scientific articles is observed, demonstrating a constant expansion of knowledge and support for future trends. Leading authors and journals are identified, highlighting global collaboration in China and India. The thematic evolution shows maturity and progressive specialization, with emerging concepts that promise future research and innovative applications.</p>
Computational linguistics based text emotion analysis using enhanced beetle antenna search with deep learning during COVID-19 pandemic
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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>
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>
Enhancing Facial Emotion Recognition: A Comparative Analysis of Sobel and Laplacian Filters for Computer Vision Applications
<p><span>This project explores the efficacy of integrating Sobel and Laplacian filters to enhance the performance of Convolutional Neural Network (CNN) models for facial emotion recognition. The project was part of our final Mtech in Data Science thesis at the Institute of Defence Institute of Advanced Technology, Pune. The FER2013 dataset was utilized for the research.</span></p>
BHAAV (भाव) - A Text Corpus for Emotion Analysis from Hindi Stories
<p>The first and largest Hindi text corpus, named BHAAV (भाव), which means emotions in Hindi, for analyzing emotions that a writer expresses through his characters in a story, as perceived by a narrator/reader. The corpus consists of 20,304 sentences collected from 230 different short stories spanning across 18 genres such as प्रेरणादायक (Inspirational) and रहस्यमयी (Mystery). Each sentence has been annotated into one of the five emotion categories anger, joy, suspense, sad, and neutral) by three native Hindi speakers with at least ten years of formal education in Hindi.</p>
statistical analysis and data: How personality shapes gaze behavior without compromising subtle emotion recognition.
<pre>CODE:<br>script_gca_X.R: scripts used for the growth curve analysis fits<br>script_fits_brms_no_tb.r: script containing the fits (except those related to the crowth curve analysis).<br>inpact_script_contrasts.r: contrasts of every fits (call inpact_script_plot.r and inpact_script_save.r)<br>inpact_script_plot.r: plots<br>inpact_script_save: save contrasts to csv and xlsx files<br><br>DATA:<br>cluster_df.Rda: personality <br>neutral.Rda: neutral trials<br>sdtg.Rda: signal detection theory parameters<br>resp_emo.Rda : raw data to emotional trials<br>df_et4_propn.Rda: eye tracking data, first exposure phase of the emotional trials (0-1000ms)<br>df_et4_prop.Rda: eye tracking data, second exposure phase of the emotional trials (1000-2000ms)<br>df_n4_propn.Rda: eye tracking data, first exposure phase of the neutral trials (0-1000ms)<br>df_n4_prop.Rda: eye tracking data, second exposure phase of the neutral trials (1000-2000ms)<br><br>gca_e_contrast.Rda: contrasts of the growth curve analysis fit for the eye Area of Interest (AOI)<br>gca_n_contrast.Rda: contrasts of the growth curve analysis fit for the nose AOI<br>gca_m_contrast.Rda: contrasts of the growth curve analysis fit for the mouth AOI<br><br>X.rds: fits<br><br><br>STIMULI:<br>- videos of the neutral and emotional facial expressions<br>- backward masks</pre>
Data for study "AI-Driven Facial Emotion Analysis for Customer Emotion Recognition - an explanatory study".
<p>This study aims to determine whether it is possible to use a commercial tool like Morphcast SDK AI to identify images that evoke more positive emotions, and thus contribute to a better perception of the hotel facility by customers, and whether it is possible to identify common elements of these photographs. The dataset was collected using Morphcast SDK AI with users.</p>
NEMO: A Database for Emotion Analysis Using Functional Near-infrared Spectroscopy
<p>The data from publication "NEMO: A Database for Emotion Analysis Using Functional Near-infrared Spectroscopy".<br> <br> <code>nemo-bids.zip</code> contains the raw optical density (OD) recordings and corresponding metadata for each participant.</p> <p><code><task_id>_csv.zip</code> provides an easy way to access the processed <a href="https://mne.tools/stable/auto_tutorials/epochs/10_epochs_overview.html">epochs</a> data without needing any code from the code repository or other BIDS tools.</p> <p><code>NEMO_additional_metadata.tsv</code> contains additional details, such as subject's age, monitor refresh rate, gender, handedness, recording date and time, specifics about different trial types, and more. Detailed descriptions of each column can be found in the <code>NEMO_additional_metadata_column_descriptions.tsv</code> file.</p> <p>For how to use the data, refer to <a href="https://github.com/Cognitive-Computing-Group/NEMO">https://github.com/Cognitive-Computing-Group/NEMO</a></p>
Chinese Metaphor Recognition and Emotion Analysis Dataset
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Unveiling Developers' Feelings: A Benchmarking Study on Emotion and Sentiment Analysis of Software Commit Messages
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Profiles of Social and Emotional Learning Skills and Learning Environment Factors in Adolescents: A Latent Profile Analysis
<p>data</p>
Dataset of the Distress Screening Scale for the article: Emotional distress, coping, and social support: A network analysis of risk and resources in persons with cancer
<p>Dataset of the Distress Screening System comprises 52 items that relate to six psycho-social domains of human functioning: depression, anxiety, social support, coping efficacy, satisfaction with care, and functional status</p>
Emotions Analysis in Patients With Chronic Phase of Stroke Basal Ganglia
ClinicalTrials.gov study NCT02884882. IPD Sharing: NO. Countries: 1. Publications: 0.
Analysis of Protein and Emotional Alterations During and After Hypobaric Hypoxia
ClinicalTrials.gov study NCT03823677. IPD Sharing: NO. Countries: 1. Publications: 0.
A Database for Emotion Analysis Using Physiological and Psychological Assessment by 40FY
ClinicalTrials.gov study NCT06172673. IPD Sharing: NO. Countries: 1. Publications: 0.
Prospective Analysis of Quality of Life, Presenteeism, and Emotional Health Due to Chronic Neck Pain in Homemakers
ClinicalTrials.gov study NCT04630119. IPD Sharing: NO. Countries: 1. Publications: 0.
ScienceDex guides
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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.