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    Wyświetlanie 1-8 z 8
    Tytuł:
    Zelenskys Image in Russian and Ukrainian News: Presidential Campaign 2019 in Ukraine
    Autorzy:
    Dkhair, Katrin
    Klochko, Polina
    Powiązania:
    https://bibliotekanauki.pl/articles/2042960.pdf
    Data publikacji:
    2021-06-21
    Wydawca:
    Polskie Towarzystwo Komunikacji Społecznej
    Tematy:
    Zelensky
    discourse
    media
    network analysis
    topic modeling
    Opis:
    The work explores the portrayal of the sixth president of Ukraine, Volodymyr Zelensky, in Russian and Ukrainian media sources during the pre-electoral campaign in 2019. The study used network analysis, n-grams’ generation, and LDA-based topic modeling. The study reveals that Russia’s media focused on Zelensky as a media personality, while Ukrainian sources paid attention to the portrayal of a novel popular politician. The target audience of the candidate’s campaign was the Russian-speaking population of Ukraine. Media in Ukraine’s native language were more inclined to mention elections, the role of the other candidate Petro Poroshenko and the nationalist mood, while defining Zelensky as just an ordinary candidate in an electoral race. The article is based on academic resources concerning the history of the development of political and media contexts in Ukraine, paying particular attention to agenda-setting, framing and priming techniques, and the personality of Volodymyr Zelensky.
    Źródło:
    Central European Journal of Communication; 2021, 14, 1(28); 62-76
    1899-5101
    Pojawia się w:
    Central European Journal of Communication
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Mapping the COVID-19 Anti‑Vaccination Communities on Facebook in Czechia
    Autorzy:
    Kaňková, Jaroslava
    Boomgaarden, Hajo G.
    Powiązania:
    https://bibliotekanauki.pl/articles/28328154.pdf
    Data publikacji:
    2023
    Wydawca:
    Polskie Towarzystwo Komunikacji Społecznej
    Tematy:
    COVID-19
    Czechia
    anti-vaccination
    Facebook
    topic modeling
    Opis:
    The COVID-19 pandemic has led to a rise in opposition to vaccination, hindering herd immunity. As social media play a major role in the formation of anti-vaccination communities, it is critical to monitor the discourse on the platforms to effectively counter the negative sentiment and encourage people to vaccinate. This study employs computational content analysis, specifically topic modeling and time series analysis, to monitor the COVID-19 anti-vaccination communities on Facebook in Czechia. The analysis generated 18 topics with politics, governance, and international affairs being the most discussed, and only five dealt with issues directly related to COVID-19. Discussions about information and its credibility were prevalent, and members of these anti-vaccination communities relied heavily on social media content and conspiracy websites as sources of information, while neglecting scientific resources. The study highlights the need for ongoing monitoring of anti-vaccination communities on social media and the development of effective communication strategies to promote vaccination.
    Źródło:
    Central European Journal of Communication; 2023, 16, 2(34); 186-208
    1899-5101
    Pojawia się w:
    Central European Journal of Communication
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Building semantic user profile for polish web news portal
    Autorzy:
    Misztal-Radecka, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/305619.pdf
    Data publikacji:
    2018
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    user profiling
    word embeddings
    topic modeling
    natural language processing
    gender prediction
    Opis:
    The aim of this research is to construct meaningful user profiles that are the most descriptive of user interests in the context of the media content that they browse. We use two distinct state-of-the-art numerical text-representation techniques: LDA topic modeling and Word2Vec word embeddings. We train our models on the collection of news articles in Polish and compare them with a model built on a general language corpus. We compare the performance of these algorithms on two practical tasks. First, we perform a qualitative analysis of the semantic relationships for similar article retrieval, and then we evaluate the predictive performance of distinct feature combinations for user gender classification. We apply the algorithms to the real-world dataset of Polish news service Onet. Our results show that the choice of text representation depends on the task –Word2Vec is more suitable for text comparison, especially for short texts such as titles. In the gender classification task, the best performance is obtained with a combination of features: topics from the article text and word embeddings from the title.
    Źródło:
    Computer Science; 2018, 19 (3); 307--332
    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:
    Unsupervised dynamic topic model for extracting adverse drug reaction from health forums
    Autorzy:
    Eslami, Behnaz
    Motlagh, Mehdi Habibzadeh
    Rezaei, Zahra
    Eslami, Mohammad
    Amini, Mohammad Amin
    Powiązania:
    https://bibliotekanauki.pl/articles/117691.pdf
    Data publikacji:
    2020
    Wydawca:
    Polskie Towarzystwo Promocji Wiedzy
    Tematy:
    Deep Learning
    topic modeling
    Text Mining
    ADR
    NMF
    analiza tekstu
    uczenie maszynowe
    modelowanie tematyczne
    Opis:
    The relationship between drug and its side effects has been outlined in two websites: Sider and WebMD. The aim of this study was to find the association between drug and its side effects. We compared the reports of typical users of a web site called: “Ask a patient” website with reported drug side effects in reference sites such as Sider and WebMD. In addition, the typical users’ comments on highly-commented drugs (Neurotic drugs, Anti-Pregnancy drugs and Gastrointestinal drugs) were analyzed, using deep learning method. To this end, typical users’ comments on drugs' side effects, during last decades, were collected from the website “Ask a patient”. Then, the data on drugs were classified based on deep learning model (HAN) and the drugs’ side effect. And the main topics of side effects for each group of drugs were identified and reported, through Sider and WebMD websites. Our model demonstrates its ability to accurately describe and label side effects in a temporal text corpus by a deep learning classifier which is shown to be an effective method to precisely discover the association between drugs and their side effects. Moreover, this model has the capability to immediately locate information in reference sites to recognize the side effect of new drugs, applicable for drug companies. This study suggests that the sensitivity of internet users and the diverse scientific findings are for the benefit of distinct detection of adverse effects of drugs, and deep learning would facilitate it.
    Źródło:
    Applied Computer Science; 2020, 16, 1; 41-59
    1895-3735
    Pojawia się w:
    Applied Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Identification of desired project manager competence using text mining analysis
    Autorzy:
    Wyskwarski, Marcin
    Powiązania:
    https://bibliotekanauki.pl/articles/1845057.pdf
    Data publikacji:
    2020
    Wydawca:
    Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
    Tematy:
    text mining
    competencies
    project manager
    word cloud
    topic modeling
    eksploracja tekstu
    kompetencje
    kierownik projektu
    chmura słów
    modelowanie tematyczne
    Opis:
    Purpose: An attempt to identify the competencies of the project manager desired by the employers and to determine whether changes have occurred over time. Design/methodology/approach: Job offers were automatically downloaded from website with job offers. An analysis of text mining of fragments of offers describing the competence was carried out. The analysis of text mining included initial text processing, creation of corpora of analyzed documents, creation of a document-term matrix, topic modeling algorithm and the use of classic methods derived from data mining. Findings: The most frequently used words/n-grams and the correlation of selected words/ n-grams with other words/n-grams were presented in the form of drawings. Based on the frequency of words/n-grams and the correlation value, efforts were made to identify the project manager competencies. The topic modeling algorithm was used to generate topics that can also be used to identify expected project manager competencies. Research limitations/implications: Only offers written in Polish, downloaded from one websites with job offers, which had the phrase “kierownik projektu” (“project manager”) in their job title, were analyzed. Data was collected from 09 to 11 April 2018 and from 09 to 11 April 2019. Practical implications: The method applied can be used by organizations preparing for the profession of a project manager, to modify and better adapt curricula to the needs of the labor market. Originality/value: Studies have shown that text mining of job offers can, to some extent, help determine the desired project manager competence.
    Źródło:
    Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska; 2020, 149; 735-749
    1641-3466
    Pojawia się w:
    Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Text mining in the identification of duties and responsibilities of the project manager
    Autorzy:
    Wyskwarski, Marcin
    Powiązania:
    https://bibliotekanauki.pl/articles/1882989.pdf
    Data publikacji:
    2020
    Wydawca:
    Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
    Tematy:
    text mining
    duties
    responsibilities
    project manager
    word cloud
    topic modeling
    eksploracja tekstu
    obowiązki
    odpowiedzialność
    menedżer projektu
    chmura słów
    modelowanie tematyczne
    Opis:
    Purpose: An attempt to identify the duties and responsibilities of the project manager by analysing job offers from a job website. An attempt to determine whether there were any changes between 2018 and 2019. Design/methodology/approach: Text mining was performed for fragments of job offers, describing the duties and responsibilities. The text mining analysis consisted of initial processing of the text, creation of a corpus of analysed documents, construction of a word frequency matrix and use of classical methods from the data mining are. Findings: The most common words in job offers are presented, as well as their correlation with other words. With the use of the Topic modeling algorithm, hidden topics describing the analysed job offers have been generated. These topics can also be used to identify the duties and responsibilities of a project manager. Research limitations/implications: Only the job offers meeting the following conditions were analysed: (1) they concerned the job of „project manager”; (2) the content was in Polish; (3) they were provided by www.pracuj.pl website; (4) they were collected from 09 to 11 April in 2018 and 2019. Practical implications: This method can be used by organizations training project managers, in order to modify and better adjust the curriculum to the needs of the labour market. Originality/value: Research has shown that text mining can be used to determine the responsibilities of a project manager by analysing job offers.
    Źródło:
    Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska; 2020, 144; 649-659
    1641-3466
    Pojawia się w:
    Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Whats and Hows? The Practice-Based Typology of Narrative Analyses
    Co i jak? Typologia analiz narracyjnych oparta na praktykach badawczych
    Autorzy:
    Bryda, Grzegorz
    Powiązania:
    https://bibliotekanauki.pl/articles/1371561.pdf
    Data publikacji:
    2020-08-31
    Wydawca:
    Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
    Tematy:
    analiza narracyjna
    CAQDAS
    analiza treści
    Text Mining
    kodowanie słownikowe
    modelowanie tematyczne
    Narrative Analysis
    Content Analysis
    dictionary-based coding
    topic modeling
    Opis:
    The nature of qualitative research practices is multiparadigmaticity which creates coexistence of different research and analytical approaches to the study of human experience in the living world. This diversity is particularly observed in the contemporary field of narrative research and data analysis. The purpose of this article is a methodological reflection on the process of developing typology and a proposition of new data-driven and practice-based typology of narrative analyses used by qualitative researchers in the lived experience research. I merge the CAQDAS, Corpus Linguistics, and Text Mining procedures to examine the analytical strategies inherited in a vivid language of English-language research articles, published in five influential qualitative methodological journals between 2002-2016. Using the dictionary-based content analysis in the coding process, hierarchical clustering, and topic modeling – a text-mining tool for discovering hidden semantic structures in a textual body – I confront Catherine Kohler Riessman’s heuristic typology with the data-driven approach in order to contribute the more coherent image of narrative analysis in the contemporary field of qualitative research. Finally, I propose a new model of thinking about the typology of narrative analyses based upon research practices.
    Istotą jakościowych praktyk badawczych jest wieloparadygmatyczność, która rodzi współistnienie różnych podejść metodologicznych w analizie i badaniu ludzkich doświadczeń w świecie życia codziennego. Różnorodność ta jest szczególnie widoczna w dziedzinie badań i analizy danych narracyjnych. Celem artykułu jest refleksja metodologiczna nad tworzeniem typologii analiz narracyjnych i zarazem propozycja nowego sposobu typologizacji podejść analitycznych, opartego na łączeniu lingwistyki korpusowej i przetwarzania języka naturalnego z procedurami CAQDAS, analizy treści i Text Mining. Typologia ta jest oparta na analizie narracyjnych praktyk badawczych odzwierciedlonych w języku anglojęzycznych artykułów opublikowanych w pięciu uznanych na świecie jakościowych czasopismach metodologicznych w latach 2002–2016. W artykule wykorzystuję metodę słownikową w procesie kodowania artykułów, hierarchiczne grupowanie i modelowanie tematyczne w celu odkrywania w tych publikacjach różnych typów analiz narracyjnych i badania relacji semantycznych między nimi. Jednocześnie konfrontuję heurystyczną typologię Riessmana z podejściem opartym na lingwistyce i eksploracji danych w celu rozwijania spójnego obrazu metodologii analizy narracyjnej we współczesnej dziedzinie badań jakościowych. Ostatecznie przedstawiam nowy model myślenia o analizie narracyjnej.
    Źródło:
    Przegląd Socjologii Jakościowej; 2020, 16, 3; 120-142
    1733-8069
    Pojawia się w:
    Przegląd Socjologii Jakościowej
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-8 z 8

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