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12 results for “news headline”
Dataset: Sentiment Analysis annotation of News headlines covering the Olympic legacy of Rio 2016 and London 2012 published by the Brazilian and British online media
<p>Dataset of 464 news headlines with sentiment manually annotated by a domain expert using the labels positive, negative and neutral. Data contains URLs for news articles published between 2004-2020 by the British and Brazilian media in English and Brazilian Portuguese covering the Olympic legacies of London 2012 and Rio 2016. Articles were collected from the news outlets’ websites using Google search engine.</p> <p>News outlets:</p> <ul> <li>The Guardian</li> <li>Daily Mail</li> <li>Globo</li> <li>Estadao</li> </ul>
Sentiment Analysis outputs based on the combination of three classifiers for news headlines and body text
<p>Sentiment Analysis outputs based on the combination of three classifiers for news headlines and body text covering the Olympic legacy of Rio 2016 and London 2012. Data was searched via Google search engine. It is composed of sentiment labels assigned to 1271 news articles in total.</p> <p><strong>News outlets:</strong></p> <ul> <li>BBC</li> <li>Daily Mail</li> <li>The Telegraph</li> <li>The Guardian</li> <li>Globo</li> <li>Estadao</li> <li>Folha de S. Paulo</li> </ul> <p><strong>Events covered by the articles:</strong></p> <ul> <li>London 2012 Olympic legacy</li> <li>Rio 2016 Olympic legacy</li> </ul> <p>All classifiers were used in texts in English. Text originally published in Portuguese by the Brazilian media were automatically translated.</p> <p><strong>Sentiment classifiers used:</strong></p> <ul> <li>Vader</li> <li>BERT (Trained on Amazon data)</li> <li>BERT (Trained on twitter data - 140)</li> </ul> <p>Each document (spreadsheet - xlsx) refers to one outlet and one event (London 2012 or Rio 2016).</p> <p><strong>How were labels assigned to the texts?</strong></p> <p>These labels are a combination of the three sentiment classifiers listed above. If two of them agree with the same label, then this label would be considered as right. Otherwise, the label ‘other’ was assigned.</p> <p>For news article body text: the proportion of sentences of each sentiment type was used to assign labels to the whole article instead of averaging the sentence scores. For example, if the proportion of sentences with negative labels is greater than 50%, then the article is assigned a negative label.</p> <p><strong>The documents are composed of the following columns:</strong></p> <ul> <li>Rank: the position of the article on Google search ranking</li> <li>Date: date of article's publication (DD/MM/YYYY)</li> <li>Link: article's link</li> <li>Title: article's title</li> <li>Sentiment_Title: final sentiment for article headline</li> <li>Sentiment_Text: final sentiment for article's body text</li> </ul> <p><em>PS: Documents do not include articles' body text. </em></p> <p><strong>Sentiment is presented in labels as follows:</strong></p> <ul> <li>Pos: Positive</li> <li>Neg: Negative</li> <li>Neutral: Neutral</li> <li>other: inconclusive - if each of the 3 classifiers assigned a different label to the article, the label 'other' was used. Therefore, 'other' identifies contradictory results.</li> </ul> <p> </p>
News headlines of BBC articles published by @BBCBreaking twitter account
<p>The dataset consists of a list of news articles headlines retrieved from tweets published by @BBCBreaking profile in specific years (2012, 2015, 2017, 2019 and 2022).</p> <p>The dataset is in <code>.csv</code> format and is organised as follows:</p> <ul> <li>Columns: <ul> <li>ID (tweet ID)</li> <li>created_at (tweet publication's date)</li> <li>url (url of the news article attached to the tweet)</li> <li>Titles (news headline)</li> </ul> </li> <li>Rows: Each row contains a single news article headline sorted by date of publication (created_at). Total number of entries: 7213.</li> </ul> <p>For more details about data collection refer to <a href="https://github.com/caiocmello/news-mood">Github</a>.</p>
IsiZulu News (articles and headlines) and Siswati News (headlines) Corpora - za-isizulu-siswati-news-2022
<p>IsiZulu News (articles and headlines) and Siswati News (headlines) Corpora - za-isizulu-siswati-news-2022</p> <p>Reference paper</p> <p>Madodonga, A., Marivate, V., & Adendorff, M. (2023). Izindaba-Tindzaba: Machine learning news categorisation for Long and Short Text for isiZulu and Siswati. <em>Journal of the Digital Humanities Association of Southern Africa</em>, <em>4</em>(01). https://doi.org/10.55492/dhasa.v4i01.4449</p> <p> </p> <p>> @article{Madodonga_Marivate_Adendorff_2023, title={Izindaba-Tindzaba: Machine learning news categorisation for Long and Short Text for isiZulu and Siswati}, volume={4}, url={https://upjournals.up.ac.za/index.php/dhasa/article/view/4449}, DOI={10.55492/dhasa.v4i01.4449}, author={Madodonga, Andani and Marivate, Vukosi and Adendorff, Matthew}, year={2023}, month={Jan.} }</p>
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>
An Evaluation Framework for Mapping News Headlines to Event Classes in a Knowledge Graph
<p>News headline to event classes corpus derived from Wikidata.</p> <p>Github: https://github.com/mbouadeus/news-headline-event-linking</p>
SemEval-2020 Task 7: Assessing Humor in Edited News Headlines
<p>This is the task dataset for SemEval-2020 Task 7: Assessing Humor in Edited News Headlines.</p> <p>The task’s dataset contains news headlines in which short edits were applied to make them funny, and the funniness of these edited headlines was rated using crowdsourcing. This task includes two subtasks, the first of which is to estimate the funniness of headlines on a humor scale in the interval 0-3. The second subtask is to predict, for a pair of edited versions of the same original headline, which is the funnier version.</p> <p>CodaLab page hosting the competition:<br> <a href="https://competitions.codalab.org/competitions/20970">https://competitions.codalab.org/competitions/20970</a></p> <p>Starter Github code (scripts for running baseline and evaluation):<br> <a href="https://github.com/n-hossain/semeval-2020-task-7-humicroedit">https://github.com/n-hossain/semeval-2020-task-7-humicroedit</a></p> <p>Task mailing list:<br> <a href="https://groups.google.com/forum/#!forum/semeval-2020-task-7-all">https://groups.google.com/forum/#!forum/semeval-2020-task-7-all</a><br> ----------------------------------------------------------------------</p> <p>ZIP contents:<br> -------------</p> <p>Folders:<br> - subtask-1: Dataset for the funniness regression subtask.<br> - subtask-2: Dataset for the "Funnier of the Two" classification subtask.</p> <p>Files:<br> - {train, dev, test}.csv: the task's dataset including labels<br> - train_funlines.csv: additional training data gathered from the FunLines competition (https://funlines.co)<br> - baseline.zip: contains csv file which is the output of the BASELINE system. This is a template of the output format that can be submitted to CodaLab for scoring.</p> <p><strong>Reference</strong></p> <p>Please cite the task paper when using this dataset:</p> <p>Nabil Hossain, John Krumm, Michael Gamon and Henry Kautz. 2020. Semeval-2020 Task 7: Assessing Humor in Edited News Headlines. In Proceedings of International Workshop on Semantic Evaluation (SemEval-2020).</p> <pre><code>BIBTEX: @InProceedings{hossainSemEval2020Task7, author = {Hossain, Nabil and Krumm, John and Gamon, Michael and Kautz,Henry}, title = {SemEval-2020 {T}ask 7: {A}ssessing Humor in Edited News Headlines}, booktitle = {Proceedings of the 14th International Workshop on Semantic Evaluation ({S}em{E}val-2020)}, address = {Barcelona, Spain}, year = {2020}}</code></pre> <p> </p>
singh1998anurag/Sentiment-Analysis-of-News-Headlines-Using-Simple-Transformers: Release of Dataset of News Headlines
<p>Dataset of News Headlines for performing Sentiment Analysis using Simple Transformers.</p>
News Headline Sentiment Dataset
<p>This dataset is part of the Monash, UEA & UCR time series regression repository. <a href="http://tseregression.org/">http://tseregression.org/</a></p> <p>The goal of this dataset is to predict sentiment score for news headline. This dataset contains 83164 time series obtained from the News Popularity in Multiple Social Media Platforms dataset from the UCI repository. This is a large data set of news items and their respective social feedback on multiple platforms: Facebook, Google+ and LinkedIn. The collected data relates to a period of 8 months, between November 2015 and July 2016, accounting for about 100,000 news items on four different topics: economy, microsoft, obama and palestine. This data set is tailored for evaluative comparisons in predictive analytics tasks, although allowing for tasks in other research areas such as topic detection and tracking, sentiment analysis in short text, first story detection or news recommendation. The time series has 3 dimensions. <br> <br> Please refer to <a href="https://archive.ics.uci.edu/ml/datasets/News+Popularity+in+Multiple+Social+Media+Platforms">https://archive.ics.uci.edu/ml/datasets/News+Popularity+in+Multiple+Social+Media+Platforms</a> for more details<br> <br> Citation request<br> Nuno Moniz and Luis Torgo (2018), Multi-Source Social Feedback of Online News Feeds, CoRR</p>
Rewritten Media Bias News Headlines
<p>This dataset was used in the paper "Rewriting Bias: Mitigating Media Bias in News Recommender Systems through Automated Rewriting" by Qin Ruan, Jin Xu, Susan Leavy, Brian Mac Namee, and Ruihai Dong, presented at the 32nd ACM Conference on User Modeling, Adaptation and Personalization (UMAP'24), July 1-4, 2024, in Cagliari, Italy. (ACM, New York, NY, USA, 10.1145/3627043.3659541).</p> <p>The dataset includes seven distinct rewritten versions of news headlines generated using the following methods proposed in the paper: RADJ, RADV, RNOUN, RVERN, RALL, RG3.5, and RG4.0.</p> <p>The rewriting approaches are categorised into two main categories: <strong>Word Replacement </strong>approaches and <strong>Large Language Models </strong>approaches.</p> <p>1.Sentence Rewriting Using Word Replacement :</p> <ul> <li><strong>Replace Adjectives (RADJ)</strong>: This approach replaces adjectives identified as contributing to bias with neutral or opposite ones.</li> <li><strong>Replace Adverbs (RADV)</strong>: This approach replaces adverbs identified as contributing to bias with neutral or opposite ones.</li> <li><strong>Replace Nouns (RNOUN)</strong>: This approach replaces nouns identified as contributing to bias with neutral or more general ones.</li> <li><strong>Replace Verbs (RVERB)</strong>: This approach replaces verbs identified as contributing to bias with neutral or more factual ones.</li> <li><strong>Replace All (RALL)</strong>: This is a combination of all the above approaches.</li> </ul> <p>2.Sentence Rewriting Using Large language Models:</p> <ul> <li><strong>Sentence Rewriting using GPT-3.5 (RG3.5)</strong>: This method rephrases sentences using GPT-3.5 based on a specific prompt.</li> <li><strong>Sentence Rewriting using GPT-4.0 (RG4.0)</strong>: This method rephrases sentences using GPT-4.0 based on a specific prompt.</li> </ul> <p>The dataset consists of original news headlines and their corrsponding rewritten versions prodcued by each of the seven approaches mentioned above. Each rewritten version aims to reduce bias while maintaining the original meaning.</p> <p> </p> <p><strong>Dataset Structure:</strong></p> <p>The dataset consists of a single file with the following columns:</p> <ul> <li>NewsId: A unique identifier for each news article.</li> <li>Original: The original headline of the news article.</li> <li>RG3.5: The headline rewritten using the <a href="https://platform.openai.com/docs/models/gpt-3-5">GPT-3.5</a> model.</li> <li>RG4.0: The headline rewritten using the <a href="https://platform.openai.com/docs/models/gpt-4-and-gpt-4-turbo">GPT-4.0</a> model.</li> <li>RADJ: The headline with adjectives replaced to reduce bias.</li> <li>RADV: The headline with adverbs replaced to reduce bias.</li> <li>RVERB: The headline with verbs replaced to reduce bias.</li> <li>RNOUN: The headline with nouns replaced to reduce bias.</li> <li>RALL: The headline with adjectives, adverbs, verbs. noun replaced to reduce bias.</li> </ul> <p><strong>Usage:</strong></p> <p>This dataset can be used to study the impact of different sentence rewriting approaches on reudicng bias in enws healines. It is also suitable for researchers interested in natural language processing, media bias reduction, and the application of large language models in generative AI.</p> <p>For more information, please visit <a href="https://github.com/ruanqin0706/MediaBiasinNewsRec">https://github.com/ruanqin0706/MediaBiasinNewsRec</a></p> <p><strong>Contact Information: </strong>qin.ruan@ucdconnect.ie</p>
SEN - Sentiment analysis of Entities in News headlines
<p>If you wish to use this data please cite:</p> <p>Katarzyna Baraniak, Marcin Sydow,<br> A dataset for Sentiment analysis of Entities in News headlines (SEN),<br> Procedia Computer Science,<br> Volume 192,<br> 2021,<br> Pages 3627-3636,<br> ISSN 1877-0509,<br> <a href="https://doi.org/10.1016/j.procs.2021.09.136">https://doi.org/10.1016/j.procs.2021.09.136</a>.<br> (<a href="https://www.sciencedirect.com/science/article/pii/S1877050921018755">https://www.sciencedirect.com/science/article/pii/S1877050921018755</a>)</p> <p>bibtex: <a href="http://users.pja.edu.pl/~msyd/bibtex/sydow-baraniak-SENdataset-kes21.bib">users.pja.edu.pl/~msyd/bibtex/sydow-baraniak-SENdataset-kes21.bib</a><br> </p> <p>SEN is a novel publicly available human-labelled dataset for training and testing machine learning algorithms for the problem of entity level sentiment analysis of political news headlines.</p> <p>On-line news portals play a very important role in the information society. Fair media should present reliable and objective information. In practice there is an observable positive or negative bias concerning named entities (e.g. politicians) mentioned in the on-line news headlines.<br> Our dataset consists of 3819 human-labelled political news headlines coming from several major on-line media outlets in English and Polish.</p> <p>Each record contains a news headline, a named entity mentioned in the headline and a human annotated label (one of “positive”, “neutral”, “negative” ). Our SEN dataset package consists of 2 parts: SEN-en (English headlines that split into SEN-en-R and SEN-en-AMT), and SEN-pl (Polish headlines). Each headline-entity pair was annotated via team of volunteer researchers (the whole SEN-pl dataset and a subset of 1271 English records: the SEN-en-R subset, “R” for “researchers”) or via the Amazon Mechanical Turk service (a subset of 1360 English records: the SEN-en-AMT subset).</p> <p>During analysis of annotation outlying annotations and removed . Separate version of dataset without outliers is marked by "noutliers" in data file name.</p> <p>Details of the process of preparing the dataset and presenting its analysis are presented in the paper.<br> </p> <p>In case of any questions, please contact one of the authors. Email adresses are in the paper.</p>
Data and code for project "What makes users click: the effect of news values negativity and surprise in news headlines on the CTR"
<p>This repository includes the following files and corresponding Python codes saved in Jupiter Notebooks:</p> <p><strong>upworthy-archive-confirmatory-packages-03.12.2020.csv:</strong> Original dataset downloaded from The Upworthy Research Archive (https://osf.io/jd64p/)</p> <p><strong>cleaned_data.csv</strong>: <strong>upworthy-archive-confirmatory-packages-03.12.2020.csv</strong> cleaned with <strong>notebook_clean_data.ipynb</strong></p> <ul> <li>Data sorted by <em>clickability_test_id</em></li> <li>Headlines with 0 clicks deleted</li> <li>Variables <em>ctr</em>, <em>mean_ctr</em>, and <em>lift</em> created</li> <li>Variable <em>winner</em> converted to dummy variable</li> <li>Only kept first occurrence of headlines that occur multiple times in the dataset per test</li> <li>Deleted tests with only one headline</li> </ul> <p><strong>sample_manual_annotations.csv</strong>: sample of 300 headlines of <strong>cleaned_data.csv </strong>retrieved with <strong>notebook_sample_manual_annotations.ipynb</strong></p> <p><strong>inter-annotator_sentiment.csv</strong>: input for <strong>notebook_inter-annotator_scores_confusion_matrix.ipynb</strong> to calculate inter-annotator agreement scores with regard to annotating sentiment</p> <p><strong>inter-annotator_emotion.csv</strong>: input for <strong>notebook_inter-annotator_scores_confusion_matrix.ipynb</strong> to calculate inter-annotator agreement scores with regard to annotating emotion</p> <p><strong>input_sentiment.tsv</strong>: input for <strong>notebook_sentiment_emotion_test_set.ipynb </strong>for sentiment analysis on sample of 300 headlines in <strong>sample_manual_annotations.csv</strong></p> <p><strong>input_emotion.tsv</strong>: input for <strong>notebook_sentiment_emotion_test_set.ipynb </strong>for emotion analysis on sample of 300 headlines in <strong>sample_manual_annotations.csv</strong></p> <p><strong>input_all_headlines.tsv</strong>: input for <strong>notebook_sentiment_emotion_final_annotation.ipynb</strong> to annotate all headlines in <strong>cleaned_data.csv</strong></p> <p><strong>final_dataset.csv</strong>: output from <strong>notebook_merge_dataframes.ipynb</strong> that merges all csv output files from <strong>notebook_sentiment_emotion_final_annotation.ipynb </strong>with <strong>cleaned_data.csv</strong></p> <p><strong>notebook_descriptive_statistics.ipynb</strong>: Python code to retrieve descriptive statistics of various variables in <strong>cleaned_data.csv</strong></p> <p><strong>NRC-Emotion-Lexicon-Wordlevel-v0.92.txt:</strong> NRC Emotion Lexicon used in <strong>notebook_sentiment_emotion_test_set.ipynb </strong>and<strong> notebook_sentiment_emotion_final_annotation.ipynb</strong></p>
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