Mine Your Own Business: Market-Structure Surveillance Through Text Mining

By Oded Netzer, Ronen Feldman, Jacob Goldenberg, Moshe Fresko. Marketing science (Providence, R.I.), 2012

726 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/19eb39b5b318fff983f66412f50f2672d1a7e959

How researchers see this paper

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

In Artificial Intelligence

In Sociology and Political Science

In Statistical and Nonlinear Physics

Citations in the corpus

Ranking products through online reviews: A method based on sentiment analysis technique and intuitionistic fuzzy set theory (2017, background): “…analysis and opinion mining of online texts, the government can acquire public opinions on public politics [22] , companies can conduct market research and ﬁnd weakness of product and service [23–25] , and consumers can make better decisions on the purchasing of products or services [10, 11, 24] .”
Marketing Analytics for Data-Rich Environments (2016, neutral): “Marketing models for large-scale unstructured data are still in their infancy, but research is starting to emerge (Lee and Bradlow 2011; Netzer et al. 2012).”
Revisiting James March (1991): Whither exploration and exploitation? (2018, result): “We depart from previous studies (e.g. Fourné et al., 2016; Lavie et al., 2010; Nosella et al., 2012; see Online Appendix A1) in several ways.”
Examining the impact of luxury brand's social media marketing on customer engagement: Using big data analytics and natural language processing (2019, methodology): “Moreover, we used functions provided by the MySQL database to aggregate the 3.78 million tweets into a monthly panel data set in order to integrate them into econometric models (Netzer, Feldman, Goldenberg, & Fresko, 2012).”
The impact of social and conventional media on firm equity value: A sentiment analysis approach (2013, background): “With an increasing amount of user-generated content (UGC) on the social media, more and more businesses and top executives are recognizing social media as an incredibly rich vein for gaining a better understanding of the online discussion and market opportunities, and for gaining feedback and evalua”
Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook (2018, positive): “Recent research in the social sciences has leveraged a variety of NLP methods to mine textual data and these techniques have gained traction in business research (see for e.g., Netzer et al. (2012); Archak et al. (2011); Ghose et al. (2012)).”
Mining Marketing Meaning from Online Chatter: Strategic Brand Analysis of Big Data Using Latent Dirichlet Allocation (2014, background): “Netzer et al. (2012) exploit the co-occurrence of words and use semantic network analysis to derive market structure from online consumer forums.”
Topic Modeling in Management Research: Rendering New Theory from Textual Data (2019, methodology): “Similarly, Netzer et al. (2012) used car brand clicks on the online forum Edmonds.com to identify co-occurring words in topics about different car brands.”
Mining customer requirements from online reviews: A product improvement perspective (2016, background): “In addition, manufacturers are able to draw knowledge about the market structure and competitive landscape to support their marketing decisions [48].”
Large-Scale Cross-Category Analysis of Consumer Review Content on Sales Conversion Leveraging Deep Learning (2019, neutral): “To emphasize scalability and low cost, a series of articles have proposed an unsupervised method to summarize and extract content from a massive amount of review data, to understand consumer preference and identify competitors using lexical-based semantic networks (Netzer et al. 2012); extract laten”