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12 results for “news sentiment”
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>
Arabic news credibility on Twitter using sentiment analysis and ensemble learning
<p>Arabic news credibility on Twitter using sentiment analysis and ensemble learning.</p> <p> </p> <p>WHAT IS IT?</p> <p>-----------</p> <p>an Arabic news credibility model on Twitter using sentiment analysis and ensemble learning.</p> <p>Here we include the Collected dataset and the source code of the proposed model written in Python language and using Keras library with Tensorflow backend.</p> <p> </p> <p>Required Packages</p> <p>------------------</p> <ol> <li>Keras (<a href="https://keras.io/">https://keras.io/</a>).</li> <li>Scikit-learn (<a href="http://scikit-learn.org/)">http://scikit-learn.org/)</a></li> <li>Imnlearn (<a href="https://imbalanced-learn.org/stable/">imbalanced-learn documentation — Version 0.10.1</a>)</li> </ol> <p> </p> <p> </p> <p>To Run the model</p> <p>---------------</p> <p>One data file is required to run the model which are:</p> <p> </p> <ol> <li>The data that were used are the collected dataset in the file, set the path of the required data file in the code.</li> </ol> <p> </p> <p>The dataset</p> <p>---------------</p> <ol> <li>There are the dataset file with all features, you can choose the features that you need and apply it on the model.</li> <li>There are a description file that describe each feature in the news credibility dataset</li> <li>The file Tweet_ID contains the list of tweets id in the dataset.</li> <li>The annotated replies based on credibility is provided.</li> </ol> <p> </p> <p> </p> <p> </p> <p> </p> <p>CONTACTS</p> <p>--------</p> <ul> <li>If you want to report bugs or have general queries email to <duha_atif@yahoo.com></li> </ul> <p> </p> <p> </p> <p> </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>
Forecasting with news sentiment: Evidence with UK newspapers
<p>These are datasets of economic sentiments derived from Uk newspapers using a dictionary and support vector machines. For more information on the application refer to : </p> <p>Rambaccussing, D. and Kwiatkowski, A., 2020. Forecasting with news sentiment: Evidence with UK newspapers. <em>International Journal of Forecasting</em>, <em>36</em>(4), pp.1501-1516.</p> <p>https://www.sciencedirect.com/science/article/pii/S0169207020300595</p> <p> </p> <p> </p>
Lexicon and example extensions from paper "Lexicon-based comments-oriented news sentiment analyzer system"
<p>Lexicon and example extensions for the article "Moreo, Alejandro, et al. "Lexicon-based comments-oriented news sentiment analyzer system." <em>Expert Systems with Applications</em> 39.10 (2012): 9166-9180." Founded by Ministerio de Educación y Ciencia and Junta de Andalucía with Projects: TIN2007-60199, TIC2009-5011 and TIN2007-67984</p>
Forex News Annotated Dataset for Sentiment Analysis
<p>This dataset contains news headlines relevant to key forex pairs: AUDUSD, EURCHF, EURUSD, GBPUSD, and USDJPY. The data was extracted from reputable platforms <a href="https://www.forexlive.com">Forex Live</a> and <a href="https://www.fxstreet.com/">FXstreet</a> over a period of 86 days, from January to May 2023. The dataset comprises 2,291 unique news headlines. Each headline includes an associated forex pair, timestamp, source, author, URL, and the corresponding article text. Data was collected using web scraping techniques executed via a custom service on a virtual machine. This service periodically retrieves the latest news for a specified forex pair (ticker) from each platform, parsing all available information. The collected data is then processed to extract details such as the article's timestamp, author, and URL. The URL is further used to retrieve the full text of each article. This data acquisition process repeats approximately every 15 minutes.</p> <p>To ensure the reliability of the dataset, we manually annotated each headline for sentiment. Instead of solely focusing on the textual content, we <strong>ascertained sentiment based on the potential short-term impact of the headline on its corresponding forex pair</strong>. This method recognizes the currency market's acute sensitivity to economic news, which significantly influences many trading strategies. As such, this dataset could serve as an invaluable resource for fine-tuning sentiment analysis models in the financial realm.</p> <p>We used three categories for annotation: 'positive', 'negative', and 'neutral', which correspond to bullish, bearish, and hold sentiments, respectively, for the forex pair linked to each headline. The following Table provides examples of annotated headlines along with brief explanations of the assigned sentiment. </p> Examples of Annotated Headlines Forex Pair Headline Sentiment Explanation GBPUSD Diminishing bets for a move to 12400 Neutral Lack of strong sentiment in either direction GBPUSD No reasons to dislike Cable in the very near term as long as the Dollar momentum remains soft Positive Positive sentiment towards GBPUSD (Cable) in the near term GBPUSD When are the UK jobs and how could they affect GBPUSD Neutral Poses a question and does not express a clear sentiment JPYUSD Appropriate to continue monetary easing to achieve 2% inflation target with wage growth Positive Monetary easing from Bank of Japan (BoJ) could lead to a weaker JPY in the short term due to increased money supply USDJPY Dollar rebounds despite US data. Yen gains amid lower yields Neutral Since both the USD and JPY are gaining, the effects on the USDJPY forex pair might offset each other USDJPY USDJPY to reach 124 by Q4 as the likelihood of a BoJ policy shift should accelerate Yen gains Negative USDJPY is expected to reach a lower value, with the USD losing value against the JPY AUDUSD <p>RBA Governor Lowe’s Testimony High inflation is damaging and corrosive </p> Positive Reserve Bank of Australia (RBA) expresses concerns about inflation. Typically, central banks combat high inflation with higher interest rates, which could strengthen AUD. <p>Moreover, the dataset includes two columns with the predicted sentiment class and score as predicted by the <a href="https://huggingface.co/ProsusAI/finbert">FinBERT</a> model. Specifically, the FinBERT model outputs a set of probabilities for each sentiment class (positive, negative, and neutral), representing the model's confidence in associating the input headline with each sentiment category. These probabilities are used to determine the predicted class and a sentiment score for each headline. The sentiment score is computed by subtracting the negative class probability from the positive one.</p>
Data for manuscript: "Using Word Embeddings to Probe Sentiment Associations of Politically Loaded Terms in News and Opinion Articles from News Media Outlets"
<p>This data set contains material for the purpose of scientific reproducibility of the accompanying manuscript "Using Word Embeddings to Probe Sentiment Associations of Politically Loaded Terms in News and Opinion Articles from News Media Outlets".</p> <p>Note that this data set is distributed with an Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License. NonCommercial means you may not use the material for commercial purposes. NoDerivatives means if you remix, transform, or build upon the material, you may not distribute the modified material. Attribution means you must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. See attached license terms for details.</p> <p>The work "Using Word Embeddings to Probe Sentiment Associations of Politically Loaded Terms in News and Opinion Articles from News Media Outlets" describes an analysis of political associations in 27 million diachronic (1975-2019) news and opinion articles from 47 news media outlets popular in the United States. We use embedding models trained on individual outlets content to quantify outlet-specific latent associations between positive/negative sentiment words and terms loaded with political connotations such as those describing political orientation, party affiliation, names of influential politicians and ideologically aligned public figures. </p> <p>News and opinion articles from the outlets listed in Figure 3 are available in the outlet's online domains and/or public cache repositories such as Google cache, The Internet Wayback Machine [31] and Common Crawl [32]. This work has not analyzed video or audio content of news media organizations, except when the outlet explicitly provides a transcript of such content in article form.<br> The temporal coverage of articles from different news outlets is not uniform. For most media organizations, news articles availability in their online domains or Internet cache backups becomes sparse as a function of articles’ age. This is not the case for some news outlets, where availability of news articles goes back to the 1970s. The Supplementary Material (SM) illustrates the time ranges of article data analyzed based on news outlets articles online availability.</p> <p>Textual content included in our analysis is circumscribed to the articles’ headlines and main text and does not include other article elements such as figure captions. Targeted textual content was located in HTML raw data using outlet specific XPath expressions. Tokens were lowercased prior to estimating embedding models. Markup language tags, URLs, nonalphanumeric characters, punctuation, digits, 330 common stop words and multiple spaces were removed prior to estimating word embeddings models.<br> All the analysis scripts and the diachronic word embedding models built from each of the 47 news media outlets analyzed in this work are available in this repository.</p> <p>For the purpose of reproducibility, we also provide in the above repository the articles’ text used to train the news outlets embedding models with the caveat that outlets articles not accessible without a subscription have been excluded. Also, for the included articles, stop words have been removed and the remaining words have been randomly scrambled within a sliding window of size 10 to render the articles incomprehensible to a human reader. These steps have been taken to not infringe articles copyright. These preprocessing steps have only minor impact on Continuous Bag of Words (CBOW) word2vec and the results reported in this work are similar when using the scrambled articles text to train outlet-specific embedding models.</p> <p>We derived outlet-specific word embedding models at every five-year time intervals within the 1975-2019 time range. The gensim [33] implementation of word2vec was used to train the embedding models. The continuous bag of words (CBOW) architecture performed slightly better than the Skip-Gram architecture in commonly used validation metrics so it was used for all subsequent analysis. </p> <p>For training the word embedding models, the following parameters were used: vector dimensions=300, window size=10, negative sampling=10, down sampling frequent words = 0.0001, minimum frequency count of 5 (only terms that appear more than 5 times in the corpus were included into the word embedding model vocabulary), number of training iterations (epochs) through the corpus=5. The exponent used to shape the negative sampling distribution was the default 0.75. </p> <p>Outlet-specific embedding models performance across a range of commonly used semantic, syntactic and analogy tasks was similar to popular pre-trained embedding models trained on corpora such as Twitter or Google books on similarity, association and word analogy tasks, see Supplemeentary Material of the manuscript for detailed validation tests results.</p> <p> </p>
News Title 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 title. 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> <p> </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>
Understanding Media's Role in Public Perception: Discrepancies in Sentiment During the COVID-19 Pandemic in English and Croatian News Media
Open the record for dataset details and reuse information.
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>
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