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    Wyświetlanie 1-4 z 4
    Tytuł:
    Cluo: web-scale text mining system for open source intelligence purposes
    Autorzy:
    Maciołek, P.
    Dobrowolski, G.
    Powiązania:
    https://bibliotekanauki.pl/articles/305361.pdf
    Data publikacji:
    2013
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    text mining
    big data
    OSINT
    natural language processing
    monitoring
    Opis:
    The amount of textual information published on the Internet is considered to be in billions of web pages, blog posts, comments, social media updates and others. Analyzing such quantities of data requires high level of distribution – both data and computing. This is especially true in case of complex algorithms, often used in text mining tasks. The paper presents a prototype implementation of CLUO – an Open Source Intelligence (OSINT) system, which extracts and analyzes significant quantities of openly available information.
    Źródło:
    Computer Science; 2013, 14 (1); 45-62
    1508-2806
    2300-7036
    Pojawia się w:
    Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Analysis of data pre-processing methods for sentiment analysis of reviews
    Autorzy:
    Parlar, Tuba
    Ozel, Selma
    Song, Fei
    Powiązania:
    https://bibliotekanauki.pl/articles/305513.pdf
    Data publikacji:
    2019
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    data pre-processing
    feature selection
    sentiment analysis
    text classification
    Opis:
    The goals of this study are to analyze the effects of data pre-processing methods for sentiment analysis and determine which of these pre-processing methods (and their combinations) are effective for English as well as for an agglutinative language like Turkish. We also try to answer the research question of whether there are any differences between agglutinative and non-agglutinative languages in terms of pre-processing methods for sentiment analysis. We find that the performance results for the English reviews are generally higher than those for the Turkish reviews due to the differences between the two languages in terms of vocabularies, writing styles, and agglutinative property of the Turkish language.
    Źródło:
    Computer Science; 2019, 20 (1); 123-141
    1508-2806
    2300-7036
    Pojawia się w:
    Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Impact of n-stage latent Dirichlet allocation on analysis of headline classification
    Autorzy:
    Guven, Zekeriya Anil
    Diri, Banu
    Cakaloglu, Tolgahan
    Powiązania:
    https://bibliotekanauki.pl/articles/27312901.pdf
    Data publikacji:
    2022
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    topic modeling
    headline classification
    machine learning
    text classification
    latent Dirichlet allocation
    data analysis
    Opis:
    Data analysis becomes difficult when the amount of the data increases. More specifically, extracting meaningful insights from this vast amount of data and grouping it based on its shared features without human intervention requires advanced methodologies. There are topic-modeling methods that help overcome this problem in text analyses for downstream tasks (such as sentiment analysis, spam detection, and news classification). In this research, we benchmark several classifiers (namely, random forest, AdaBoost, naive Bayes, and logistic regression) using the classical latent Dirichlet allocation (LDA) and n-stage LDA topic-modeling methods for feature extraction in headline classification. We ran our experiments on three and five classes of publicly available Turkish and English datasets. We have demonstrated that, as a feature extractor, n-stage LDA obtains state-of-the-art performance for any downstream classifier. It should also be noted that random forest was the most successful algorithm for both datasets.
    Źródło:
    Computer Science; 2022, 23 (3); 375--394
    1508-2806
    2300-7036
    Pojawia się w:
    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
      Wyświetlanie 1-4 z 4

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