Mining Marketing Meaning from Online Chatter: Strategic Brand Analysis of Big Data Using Latent Dirichlet Allocation

By Seshadri Tirunillai, Gerard J. Tellis. 2014

724 citations recorded by the source corpus.

Subfields: Marketing, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/1c8265db772792594185769d543d3cf3cdf025c6

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Citations in the corpus

When and how Managers' Responses to Online Reviews Affect Subsequent Reviews (2018, neutral): “Finally, our response tailoring analysis adds to the growing prevalence of text mining in marketing research (Tirunillai and Tellis 2014; Zhang, Moe, and Schweidel 2017).”
Navigating by the Stars: Investigating the Actual and Perceived Validity of Online User Ratings (2016, background): “There is recent evidence that narrative reviews do contain useful information about product quality (Tirunillai and Tellis 2014) and that consumers consult them (Chevalier and Mayzlin 2006).”
Sentence-Based Text Analysis for Customer Reviews (2016, background): “Alternatively, one may assume that specific words in UGC are only indicators of latent topics and that these topics are a priori unknown (Tirunillai and Tellis 2014).”
Topic Modeling in Management Research: Rendering New Theory from Textual Data (2019, methodology): “Content analysis, “a research technique for the objective, systematic, and quantitative description of the manifest content of communication” (Berelson, 1952: 18), represents arguably the most prominent and mainstream approach in this domain (Nelson, 2017; Tirunillai & Tellis, 2014).”
Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation (2017, background): “Following Tirunillai and Tellis (2014), we define a “dimension” as a latent construct distributed over a vocabulary of words that consumers use to describe their hotel experience, also referred to as a “topic” in the LDA literature.”
From Social to Sale: The Effects of Firm-Generated Content in Social Media on Customer Behavior (2016, background): “Given that UGC serves as an effective source of word of mouth (Godes 2011) and an indicator of product quality (Tirunillai and Tellis 2014), the focus on the effects of UGC in previous research is understandable.”
Machine learning and AI in marketing – Connecting computing power to human insights (2020, positive): “…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; Netzer, Feldman, Goldenberg, and Fresko, 2012; Tirunillai & Tellis, 2014; Toubia & Netzer, 2017; Zhang & Luo, 2019).”
Large-Scale Cross-Category Analysis of Consumer Review Content on Sales Conversion Leveraging Deep Learning (2019, background): “…preference and identify competitors using lexical-based semantic 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…”
Identifying Customer Needs from User-Generated Content (2019, methodology): “The research closest to our goals uses word co-occurrences and variations of Latent Dirichlet Analysis (LDA) to characterize customer preferences or latent topics in product discussions (Archak, Ghose, and Panagiotis 2016; Büschken and Allenby 2006; Lee and Bradlow 2011; Tirunillai and Tellis 2014; ”
Understanding Large-Scale Dynamic Purchase Behavior (2020, positive): “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.”
A Semantic Approach for Estimating Consumer Content Preferences from Online Search Queries (2018, background): “Applications of LDA in the marketing literature include Tirunillai and Tellis (2014), who apply LDA to identify dimensions of quality and valence expressed in online reviews;Abhishek et al. (2018),whouseLDAtomeasure the contextual ambiguity of a search keyword; and Büschken and Allenby (2017), who…”
Digital marketing: A framework, review and research agenda (2017, neutral): “Tirunillai and Tellis (2014) showed that UGC data can be mined for marketing purposes to create dynamic mapping of competitive brand positions on dimensions deemed important through the analysis of data over time.”