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|a 10.1080/02664763.2023.2247617
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|a eng
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1 |
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|a Atandoh, Paul H
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a Statistical clustering of documents via stochastic blockmodels
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| 264 |
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1 |
|c 2024
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 02.09.2024
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|a published: Electronic-eCollection
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a © 2023 Informa UK Limited, trading as Taylor & Francis Group.
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|a As the online market grows rapidly, people are relying more on product review when they purchase the product. Hence, many companies and researchers are interested in analyzing product review which essentially a text data. In the current literature, it is common to use only text analysis tools to analyze text dataset. But in our work, we propose a method that utilizes both text analysis method such as topic modeling and statistical network model to build network among individuals and find interesting communities. We introduce a promising framework that incorporates topic modeling technique to define the edges among the individuals and form a network and uses stochastic blockmodels (SBM) to find the communities. The power of our proposed method is demonstrated in real-world application to Amazon product review dataset
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| 650 |
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4 |
|a Journal Article
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| 650 |
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4 |
|a 62R07
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4 |
|a 68T50
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| 650 |
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4 |
|a Amazon product review dataset
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| 650 |
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4 |
|a Documents clustering
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| 650 |
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4 |
|a stochastic blockmodels
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| 650 |
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4 |
|a text data
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| 650 |
|
4 |
|a topic modeling
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| 700 |
1 |
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|a Lee, Kevin H
|e verfasserin
|4 aut
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|i Enthalten in
|t Journal of applied statistics
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|g 51(2024), 10 vom: 02., Seite 1878-1893
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|u http://dx.doi.org/10.1080/02664763.2023.2247617
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