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The following results are related to Digital Humanities and Cultural Heritage. Are you interested to view more results? Visit OpenAIRE - Explore.
8 Research products

  • Digital Humanities and Cultural Heritage
  • Open Access
  • European Commission
  • EC|H2020
  • EC|H2020|RIA
  • DOLFINS
  • Digital Humanities and Cultural Heritage

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Grčar, Miha; Cherepnalkoski, Darko; Mozetič, Igor; Kralj Novak, Petra;

    The corpus contains over 4.5 million tweets (tweet IDs) automatically labeled by a machine learning program with stance regarding Brexit: Positive (supporting Brexit), Negative (opposing Brexit), or Neutral (uncommitted). The Brexit referendum was held on June 23, 2016, to decide whether the UK should leave or remain in the EU. In the weeks before the referendum, starting on May 12, the UK geo-located Brexit-related tweets were continuously collected resulting in a dataset of around 4.5 million (4,508,440) tweets from almost one million (998,054) users. A large sample of the collected tweets (35,000) was manually labeled for the stance of their authors regarding Brexit: Positive (supporting Brexit), Negative (opposing Brexit), or Neutral (uncommitted). The labeled tweets were used to train a classifier which then automatically labeled all the remaining tweets. The corpus contains tweet ids and stance labels. The tweets are grouped into files one hour per file. In each file, one row represents one entry (twitter_id, sentiment_label). Lines are ordered by the tweet time. The data collection, annotation, model training and performance estimation is described in detail in: Miha Grčar, Darko Cherepnalkoski, Igor Mozetič, Petra Kralj Novak: Stance and influence of Twitter users regarding the Brexit referendum. Computational Social Networks 4/6. 2017. http://dx.doi.org/10.1186/s40649-017-0042-6

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ CLARIN.SI repositoryarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    CLARIN.SI repository
    Dataset . 2016
    License: CC BY SA
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ CLARIN.SI repositoryarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      CLARIN.SI repository
      Dataset . 2016
      License: CC BY SA
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Kralj Novak, Petra; de Amicis, Luisa; Mozetič, Igor;

    The corpus contains 668,529 tweets (tweet IDs) relevant to "impact investing", accompanied by sentiment labels given by an automated sentiment classifier. Impact investing involves investments made into companies, organizations, and funds with the intention to generate social and environmental impact alongside a financial return. The tweets relevant to impact investing were collected in the period from March 28, 2017, to January 28, 2018, through the Twitter Search API, and annotated for sentiment labels "Negative", "Neutral" or "Positive" by a general-purpose English language sentiment classifier. The tweets were collected based on a list of known impact investing Twitter users, relevant keywords and impact investing related events. In particular, the queries include relevant users (@YF_Academy, @esmeefairbairn, @resonanceltd, @Big PotentialSI, etc.), single hashtags (#socfin, #impinv #socialfinance, #impactinvestment, etc.), combined hashtags (#social & #finance, #social & #investment, #impact & #assessment, etc.), and hashtags of major impact investing events (#impact2, #socap17, #OxfordIIP, #skollwf, etc.).

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ CLARIN.SI repositoryarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    CLARIN.SI repository
    Dataset . 2018
    License: CC BY SA
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ CLARIN.SI repositoryarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      CLARIN.SI repository
      Dataset . 2018
      License: CC BY SA
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Smailovic, Jasmina; Kranjc, Janez; Grcar, Miha; Znidarsic, Martin; +1 Authors

    We present a generic approach to real-time monitoring of the Twitter sentiment and show its application to the Bulgarian parliamentary elections in May 2013. Our approach is based on building high quality sentiment classification models from manually annotated tweets. In particular, we have developed a user-friendly annotation platform, a feature selection procedure based on maximizing prediction accuracy, and a binary SVM classifier extended with a neutral zone. We have also considerably improved the language detection in tweets. The evaluation results show that before and after the Bulgarian elections, negative sentiment about political parties prevailed. Both, the volume and the difference between the negative and positive tweets for individual parties closely match the election results. The later result is somehow surprising, but consistent with the prevailing negative sentiment during the elections.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ http://dirros.opensc...arrow_drop_down
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    http://dirros.openscience.si/D...
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    https://doi.org/10.1109/dsaa.2...
    Conference object . 2015 . Peer-reviewed
    Data sources: Sygma; Crossref
    https://doi.org/10.1109/DSAA.2...
    Other literature type . Conference object . 2015
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      http://dirros.openscience.si/D...
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      https://doi.org/10.1109/dsaa.2...
      Conference object . 2015 . Peer-reviewed
      Data sources: Sygma; Crossref
      https://doi.org/10.1109/DSAA.2...
      Other literature type . Conference object . 2015
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Petra, Kralj Novak; Jasmina, Smailović; Borut, Sluban; Igor, Mozetič;

    There is a new generation of emoticons, called emojis, that is increasingly being used in mobile communications and social media. In the past two years, over ten billion emojis were used on Twitter. Emojis are Unicode graphic symbols, used as a shorthand to express concepts and ideas. In contrast to the small number of well-known emoticons that carry clear emotional contents, there are hundreds of emojis. But what are their emotional contents? We provide the first emoji sentiment lexicon, called the Emoji Sentiment Ranking, and draw a sentiment map of the 751 most frequently used emojis. The sentiment of the emojis is computed from the sentiment of the tweets in which they occur. We engaged 83 human annotators to label over 1.6 million tweets in 13 European languages by the sentiment polarity (negative, neutral, or positive). About 4% of the annotated tweets contain emojis. The sentiment analysis of the emojis allows us to draw several interesting conclusions. It turns out that most of the emojis are positive, especially the most popular ones. The sentiment distribution of the tweets with and without emojis is significantly different. The inter-annotator agreement on the tweets with emojis is higher. Emojis tend to occur at the end of the tweets, and their sentiment polarity increases with the distance. We observe no significant differences in the emoji rankings between the 13 languages and the Emoji Sentiment Ranking. Consequently, we propose our Emoji Sentiment Ranking as a European language-independent resource for automated sentiment analysis. Finally, the paper provides a formalization of sentiment and a novel visualization in the form of a sentiment bar.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Europe PubMed Centra...arrow_drop_down
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    Europe PubMed Central
    Article . 2015
    Data sources: PubMed Central
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    PLoS ONE
    Other literature type . Article . 2015 . Peer-reviewed
    License: CC BY
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    Article . 2015
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    https://doi.org/10.48550/arxiv...
    Article . 2015
    License: arXiv Non-Exclusive Distribution
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Europe PubMed Centra...arrow_drop_down
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      Europe PubMed Central
      Article . 2015
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      Other literature type . Article . 2015 . Peer-reviewed
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      Article . 2015
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      Article
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      https://doi.org/10.48550/arxiv...
      Article . 2015
      License: arXiv Non-Exclusive Distribution
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    Authors: Čerepnalkoski, Darko; Karpf, Andreas; Mozetič, Igor; Grčar, Miha;

    We study the cohesion within and the coalitions between political groups in the Eighth European Parliament (2014--2019) by analyzing two entirely different aspects of the behavior of the Members of the European Parliament (MEPs) in the policy-making processes. On one hand, we analyze their co-voting patterns and, on the other, their retweeting behavior. We make use of two diverse datasets in the analysis. The first one is the roll-call vote dataset, where cohesion is regarded as the tendency to co-vote within a group, and a coalition is formed when the members of several groups exhibit a high degree of co-voting agreement on a subject. The second dataset comes from Twitter; it captures the retweeting (i.e., endorsing) behavior of the MEPs and implies cohesion (retweets within the same group) and coalitions (retweets between groups) from a completely different perspective. We employ two different methodologies to analyze the cohesion and coalitions. The first one is based on Krippendorff's Alpha reliability, used to measure the agreement between raters in data-analysis scenarios, and the second one is based on Exponential Random Graph Models, often used in social-network analysis. We give general insights into the cohesion of political groups in the European Parliament, explore whether coalitions are formed in the same way for different policy areas, and examine to what degree the retweeting behavior of MEPs corresponds to their co-voting patterns. A novel and interesting aspect of our work is the relationship between the co-voting and retweeting patterns.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ PLoS ONEarrow_drop_down
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    PLoS ONE
    Other literature type . Article . 2016 . Peer-reviewed
    License: CC BY
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    Europe PubMed Central
    Article . 2016
    Data sources: PubMed Central
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    PLoS ONE
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    PLoS ONE
    Article . 2016
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    https://doi.org/10.48550/arxiv...
    Article . 2016
    License: arXiv Non-Exclusive Distribution
    Data sources: Datacite
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      Other literature type . Article . 2016 . Peer-reviewed
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      Europe PubMed Central
      Article . 2016
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      PLoS ONE
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      Article . 2016
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      https://doi.org/10.48550/arxiv...
      Article . 2016
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    Authors: Igor Mozetič; Miha Grčar; Jasmina Smailović;

    What are the limits of automated Twitter sentiment classification? We analyze a large set of manually labeled tweets in different languages, use them as training data, and construct automated classification models. It turns out that the quality of classification models depends much more on the quality and size of training data than on the type of the model trained. Experimental results indicate that there is no statistically significant difference between the performance of the top classification models. We quantify the quality of training data by applying various annotator agreement measures, and identify the weakest points of different datasets. We show that the model performance approaches the inter-annotator agreement when the size of the training set is sufficiently large. However, it is crucial to regularly monitor the self- and inter-annotator agreements since this improves the training datasets and consequently the model performance. Finally, we show that there is strong evidence that humans perceive the sentiment classes (negative, neutral, and positive) as ordered.

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    Europe PubMed Central
    Article . 2016
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    PLoS ONE
    Other literature type . Article . 2016 . Peer-reviewed
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    PLoS ONE
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    https://doi.org/10.48550/arxiv...
    Article . 2016
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      https://doi.org/10.48550/arxiv...
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Paolo Barucca; Marco Bardoscia; Fabio Caccioli; Marco D'Errico; +3 Authors

    We introduce a general model for the balance-sheet consistent valuation of interbank claims within an interconnected financial system. Our model represents an extension of clearing models of interdependent liabilities to account for the presence of uncertainty on banks' external assets. At the same time, it also provides a natural extension of classic structural credit risk models to the case of an interconnected system. We characterize the existence and uniqueness of a valuation that maximises individual and total equity values for all banks. We apply our model to the assessment of systemic risk, and in particular for the case of stress-testing. Further, we provide a fixed-point algorithm to carry out the network valuation and the conditions for its convergence. Comment: 23 pages, 2 figures

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    CNR ExploRA
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    Mathematical Finance
    Article . 2020 . Peer-reviewed
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    SSRN Electronic Journal
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    Mathematical Finance
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    https://doi.org/10.48550/arxiv...
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      Mathematical Finance
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      Mathematical Finance
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Cherepnalkoski, Darko; Karpf, Andreas; Mozetič, Igor; Grčar, Miha;

    The resource consists of two datasets related to Members of the 8th European Parliament (MEPs). The first one is a dataset of 2,535 roll-call votes of MEPs until 2016-03-01. The second one is a dataset of 26,133 retweets between MEPs in the period between 2014-10-01 and 2016-03-01. The data can be used to examine the patterns of covoting and retweeting of MEPs and analyze the extent to which they are similar. The resource is presented and used in the paper: Darko Cherepnalkoski, Andreas Karpf, Igor Mozetič, Miha Grčar "Cohesion and coalition formation in the European Parliament: Roll-call votes and Twitter activities". PLoS ONE 11(11): e0166586, 2016. http://dx.doi.org/10.1371/journal.pone.0166586 The dataset contains 5 files, of which 3 contain metadata and 2 data. The metadata comprises information about the Members of 8th European Parliament (MEPs) until 2016-03-01, about roll-call votes (RCV) and possible actions during a RCV. The first data file contains a matrix with the votes of all MEPs during all RCVs while the second contains the retweets between the MEPs.

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    CLARIN.SI repository
    Dataset . 2016
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      CLARIN.SI repository
      Dataset . 2016
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Grčar, Miha; Cherepnalkoski, Darko; Mozetič, Igor; Kralj Novak, Petra;

    The corpus contains over 4.5 million tweets (tweet IDs) automatically labeled by a machine learning program with stance regarding Brexit: Positive (supporting Brexit), Negative (opposing Brexit), or Neutral (uncommitted). The Brexit referendum was held on June 23, 2016, to decide whether the UK should leave or remain in the EU. In the weeks before the referendum, starting on May 12, the UK geo-located Brexit-related tweets were continuously collected resulting in a dataset of around 4.5 million (4,508,440) tweets from almost one million (998,054) users. A large sample of the collected tweets (35,000) was manually labeled for the stance of their authors regarding Brexit: Positive (supporting Brexit), Negative (opposing Brexit), or Neutral (uncommitted). The labeled tweets were used to train a classifier which then automatically labeled all the remaining tweets. The corpus contains tweet ids and stance labels. The tweets are grouped into files one hour per file. In each file, one row represents one entry (twitter_id, sentiment_label). Lines are ordered by the tweet time. The data collection, annotation, model training and performance estimation is described in detail in: Miha Grčar, Darko Cherepnalkoski, Igor Mozetič, Petra Kralj Novak: Stance and influence of Twitter users regarding the Brexit referendum. Computational Social Networks 4/6. 2017. http://dx.doi.org/10.1186/s40649-017-0042-6

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    CLARIN.SI repository
    Dataset . 2016
    License: CC BY SA
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      CLARIN.SI repository
      Dataset . 2016
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    Authors: Kralj Novak, Petra; de Amicis, Luisa; Mozetič, Igor;

    The corpus contains 668,529 tweets (tweet IDs) relevant to "impact investing", accompanied by sentiment labels given by an automated sentiment classifier. Impact investing involves investments made into companies, organizations, and funds with the intention to generate social and environmental impact alongside a financial return. The tweets relevant to impact investing were collected in the period from March 28, 2017, to January 28, 2018, through the Twitter Search API, and annotated for sentiment labels "Negative", "Neutral" or "Positive" by a general-purpose English language sentiment classifier. The tweets were collected based on a list of known impact investing Twitter users, relevant keywords and impact investing related events. In particular, the queries include relevant users (@YF_Academy, @esmeefairbairn, @resonanceltd, @Big PotentialSI, etc.), single hashtags (#socfin, #impinv #socialfinance, #impactinvestment, etc.), combined hashtags (#social & #finance, #social & #investment, #impact & #assessment, etc.), and hashtags of major impact investing events (#impact2, #socap17, #OxfordIIP, #skollwf, etc.).

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    CLARIN.SI repository
    Dataset . 2018
    License: CC BY SA
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      CLARIN.SI repository
      Dataset . 2018
      License: CC BY SA
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    Authors: Smailovic, Jasmina; Kranjc, Janez; Grcar, Miha; Znidarsic, Martin; +1 Authors

    We present a generic approach to real-time monitoring of the Twitter sentiment and show its application to the Bulgarian parliamentary elections in May 2013. Our approach is based on building high quality sentiment classification models from manually annotated tweets. In particular, we have developed a user-friendly annotation platform, a feature selection procedure based on maximizing prediction accuracy, and a binary SVM classifier extended with a neutral zone. We have also considerably improved the language detection in tweets. The evaluation results show that before and after the Bulgarian elections, negative sentiment about political parties prevailed. Both, the volume and the difference between the negative and positive tweets for individual parties closely match the election results. The later result is somehow surprising, but consistent with the prevailing negative sentiment during the elections.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ http://dirros.opensc...arrow_drop_down
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    https://doi.org/10.1109/dsaa.2...
    Conference object . 2015 . Peer-reviewed
    Data sources: Sygma; Crossref
    https://doi.org/10.1109/DSAA.2...
    Other literature type . Conference object . 2015
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      https://doi.org/10.1109/dsaa.2...
      Conference object . 2015 . Peer-reviewed
      Data sources: Sygma; Crossref
      https://doi.org/10.1109/DSAA.2...
      Other literature type . Conference object . 2015
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Petra, Kralj Novak; Jasmina, Smailović; Borut, Sluban; Igor, Mozetič;

    There is a new generation of emoticons, called emojis, that is increasingly being used in mobile communications and social media. In the past two years, over ten billion emojis were used on Twitter. Emojis are Unicode graphic symbols, used as a shorthand to express concepts and ideas. In contrast to the small number of well-known emoticons that carry clear emotional contents, there are hundreds of emojis. But what are their emotional contents? We provide the first emoji sentiment lexicon, called the Emoji Sentiment Ranking, and draw a sentiment map of the 751 most frequently used emojis. The sentiment of the emojis is computed from the sentiment of the tweets in which they occur. We engaged 83 human annotators to label over 1.6 million tweets in 13 European languages by the sentiment polarity (negative, neutral, or positive). About 4% of the annotated tweets contain emojis. The sentiment analysis of the emojis allows us to draw several interesting conclusions. It turns out that most of the emojis are positive, especially the most popular ones. The sentiment distribution of the tweets with and without emojis is significantly different. The inter-annotator agreement on the tweets with emojis is higher. Emojis tend to occur at the end of the tweets, and their sentiment polarity increases with the distance. We observe no significant differences in the emoji rankings between the 13 languages and the Emoji Sentiment Ranking. Consequently, we propose our Emoji Sentiment Ranking as a European language-independent resource for automated sentiment analysis. Finally, the paper provides a formalization of sentiment and a novel visualization in the form of a sentiment bar.

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    Europe PubMed Central
    Article . 2015
    Data sources: PubMed Central
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    PLoS ONE
    Other literature type . Article . 2015 . Peer-reviewed
    License: CC BY
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    Article . 2015
    Data sources: DOAJ-Articles
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    PLoS ONE
    Article
    License: CC BY
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    https://doi.org/10.48550/arxiv...
    Article . 2015
    License: arXiv Non-Exclusive Distribution
    Data sources: Datacite
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