Sentence-Based Text Analysis for Customer Reviews

By Joachim Büschken, Greg M. Allenby. Marketing science (Providence, R.I.), 2016

314 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Machine learning and AI in marketing – Connecting computing power to human insights (2020, unknown): “In marketing, a reasonably large literature now exists on analyzing textual data (Ansari, Li, and Zhang, 2018; Büschken & Allenby, 2016; Chakraborty, Kim, and Sudhir, 2019; Hartmann, Huppertz, et al., 2019; Lee & Bradlow, 2011; Li & Ma, 2019; Liu, Lee, & Srinivasan, 2018; Liu & Toubia, 2018;…”
Digital marketing: A framework, review and research agenda (2017, unknown): “While sentiment analysis has been used to capture valence information, only a few empirical papers have tapped into the unstructured textual content of online reviews (for example, Tirunillai & Tellis, 2014; Büschken & Allenby, 2016).”
Understanding Large-Scale Dynamic Purchase Behavior (2020, unknown): “Most of these papers involve analyzing textual data (Tirunillai and Tellis 2014, Büschken and Allenby 2016, Rutz et al. 2017, Puranam et al. 2017, Liu and Toubia 2018, Büschken and Allenby 2020), which is the traditional application of a topic model.”
Large-Scale Cross-Category Analysis of Consumer Review Content on Sales Conversion Leveraging Deep Learning (2019, background): “…networks (Netzer et al. 2012); extract latent dimensions of consumer satisfaction (Tirunillai and Tellis 2014) using latent Dirichlet allocation (Blei, Ng, and Jordan 2003); and better understand what consumers are saying, by extending latent Dirichlet allocation (Buschken and Allenby 2016).”