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A Three-Stage method for Data Text Mining: Using UGC in Business Intelligence Analysis

1
Department of Business Economics, Faculty of Social Sciences and Law, Rey Juan Carlos University, Paseo Artilleros s/n, 28032 Madrid, Spain
2
Ehrenberg Centre for Research in Marketing, London South Bank University, 103 Borough Rd., London SE1 0AA, UK
*
Author to whom correspondence should be addressed.
Symmetry 2019, 11(4), 519; https://doi.org/10.3390/sym11040519
Received: 6 March 2019 / Revised: 4 April 2019 / Accepted: 8 April 2019 / Published: 10 April 2019
The global development of the Internet, which has enabled the analysis of large amounts of data and the services linked to their use, has led companies to modify their business strategies in search of new ways to increase marketing productivity and profitability. Many strategies are based on business intelligence (BI) and marketing intelligence (MI) that make it possible to extract profitable knowledge and insights from large amounts of data generated by company customers in digital environments. In this context, the present study proposes a three-step research methodology based on data text mining (DTM). In further research, this methodology can be used for business intelligence analysis (BIA) strategies to analyze user generated content (UGC) in social networks and on digital platforms. The proposed methodology unfolds in the following three stages. First, a Latent Dirichlet Allocation (LDA) model that determines the database topic is used. Second, a sentiment analysis (SA) is proposed. This SA is applied to the LDA results to divide the topics identified in the sample into three sentiments. Thirdly, textual analysis (TA) with data text mining techniques is applied on the topics in each sentiment. The proposed methodology offers important advances in data text mining in terms of accuracy, reliability and insight generation for both researchers and practitioners seeking to improve the BIA processes in business and other sectors. View Full-Text
Keywords: data text mining; sentiment analysis; business intelligence; marketing intelligence data text mining; sentiment analysis; business intelligence; marketing intelligence
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MDPI and ACS Style

Saura, J.R.; Bennett, D.R. A Three-Stage method for Data Text Mining: Using UGC in Business Intelligence Analysis. Symmetry 2019, 11, 519.

AMA Style

Saura JR, Bennett DR. A Three-Stage method for Data Text Mining: Using UGC in Business Intelligence Analysis. Symmetry. 2019; 11(4):519.

Chicago/Turabian Style

Saura, Jose R.; Bennett, Dag R 2019. "A Three-Stage method for Data Text Mining: Using UGC in Business Intelligence Analysis" Symmetry 11, no. 4: 519.

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