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    Wyświetlanie 1-3 z 3
    Tytuł:
    Survival analysis on data streams: Analyzing temporal events in dynamically changing environments
    Autorzy:
    Shaker, A.
    Hüllermeier, E.
    Powiązania:
    https://bibliotekanauki.pl/articles/331440.pdf
    Data publikacji:
    2014
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    data stream
    survival analysis
    event history analysis
    earthquake data
    Twitter data
    strumień danych
    analiza przeżycia
    Opis:
    In this paper, we introduce a method for survival analysis on data streams. Survival analysis (also known as event history analysis) is an established statistical method for the study of temporal “events” or, more specifically, questions regarding the temporal distribution of the occurrence of events and their dependence on covariates of the data sources. To make this method applicable in the setting of data streams, we propose an adaptive variant of a model that is closely related to the well-known Cox proportional hazard model. Adopting a sliding window approach, our method continuously updates its parameters based on the event data in the current time window. As a proof of concept, we present two case studies in which our method is used for different types of spatio-temporal data analysis, namely, the analysis of earthquake data and Twitter data. In an attempt to explain the frequency of events by the spatial location of the data source, both studies use the location as covariates of the sources.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2014, 24, 1; 199-212
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Adaptation of domain-specific transformer models with text oversampling for sentiment analysis of social media posts on Covid-19 vaccine
    Autorzy:
    Bansal, Anmol
    Choudhry, Arjun
    Sharma, Anubhav
    Susan, Seba
    Powiązania:
    https://bibliotekanauki.pl/articles/27312860.pdf
    Data publikacji:
    2023
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    Covid-19
    vaccine
    transformer
    Twitter
    BERTweet
    CT-BERT
    BERT
    XLNet
    RoBERTa
    text oversampling
    LMOTE
    class imbalance
    small sample data set
    Opis:
    Covid-19 has spread across the world and many different vaccines have been developed to counter its surge. To identify the correct sentiments associated with the vaccines from social media posts, we fine-tune various state-of-the-art pretrained transformer models on tweets associated with Covid-19 vaccines. Specifically, we use the recently introduced state-of-the-art RoBERTa, XLNet, and BERT pre-trained transformer models, and the domain-specific CT-BER and BERTweet transformer models that have been pre-trained on Covid-19 tweets. We further explore the option of text augmentation by oversampling using the language model-based oversampling technique (LMOTE) to improve the accuracies of these models - specifically, for small sample data sets where there is an imbalanced class distribution among the positive, negative and neutral sentiment classes. Our results summarize our findings on the suitability of text oversampling for imbalanced, small-sample data sets that are used to fine-tune state-of-the-art pre-trained transformer models as well as the utility of domain-specific transformer models for the classification task.
    Źródło:
    Computer Science; 2023, 24 (2); 163--182
    1508-2806
    2300-7036
    Pojawia się w:
    Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Social media analysis of the public perception of urban vehicle access regulations
    Autorzy:
    Ogunkunbi, Gabriel
    Meszaros, Ferenc
    Powiązania:
    https://bibliotekanauki.pl/articles/27314057.pdf
    Data publikacji:
    2023
    Wydawca:
    Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
    Tematy:
    UVAR
    congestion charging
    emission zone
    sentiment analysis
    social network data
    Twitter
    opłaty za wjazd
    strefa emisji
    analiza nastrojów
    dane sieci społecznościowych
    Opis:
    Heavy motorisation in the wake of increasing urbanisation is one of the significant transport problems cities face today. There are practical measures under the panoply of urban vehicle access regulations (UVARs) used to stimulate sustainable mobility behaviour changes in the urban population and reduce reliance on passenger car travel. However, the adoption and implementation of such measures are often riddled with challenges, particularly building public acceptability and preserving social justice. Overcoming these challenges will also require cities to understand how the mobility needs of residents change over time. Considering the limitations of conventional data-collection and monitoring approaches, this study explored and analysed the public perception of UVARs over 12 years through natural language processing techniques using social media as a data source. The results show that UVARs are a prominent topic in public discussion and that the average sentiment expressed in tweets tended to be more positive than negative, with a gradual increase observed over the 12-year study period. In addition, the patterns observed in the data and the topics modelled were consistent with the events and talking points in society related to UVARs. Hence, this study demonstrates that social media data can help policymakers assess public sentiments during the ideation, design, implementation, and operational phases of UVARs and other transport policy measures.
    Źródło:
    Transport Problems; 2023, 18, 1; 157--168
    1896-0596
    2300-861X
    Pojawia się w:
    Transport Problems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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