Atandoh Paul H, Lee Kevin H
Department of Mathematics, Mercer University, Macon, GA, USA.
Department of Statistics, Western Michigan University, Kalamazoo, MI, USA.
J Appl Stat. 2023 Sep 1;51(10):1878-1893. doi: 10.1080/02664763.2023.2247617. eCollection 2024.
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.
随着在线市场的迅速发展,人们在购买产品时越来越依赖产品评论。因此,许多公司和研究人员都对分析产品评论感兴趣,而产品评论本质上是一种文本数据。在当前的文献中,通常只使用文本分析工具来分析文本数据集。但在我们的工作中,我们提出了一种方法,该方法利用主题建模等文本分析方法和统计网络模型来构建个体之间的网络并找到有趣的社区。我们引入了一个有前景的框架,该框架结合主题建模技术来定义个体之间的边并形成一个网络,并使用随机块模型(SBM)来找到社区。我们所提出方法的优势在对亚马逊产品评论数据集的实际应用中得到了证明。