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

Journal article


Bennett, D and Saura Lacárcel, JR (2019). A Three-Stage method for Data Text Mining: Using UGC in Business Intelligence Analysis. Symmetry. 11 (4), p. 519.
AuthorsBennett, D and Saura Lacárcel, JR
Abstract

The global development of the Internet, which 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 negative, positive, and neutral 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.

KeywordsData-Mining; User-Generated-Content; Machine learning; Nvivo
Year2019
JournalSymmetry
Journal citation11 (4), p. 519
PublisherMDPI
ISSN2073-8994
Digital Object Identifier (DOI)doi:10.3390/sym11040519
Publication dates
Print10 Apr 2019
Publication process dates
Deposited17 Apr 2019
Accepted08 Apr 2019
Accepted author manuscript
License
CC BY 4.0
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https://openresearch.lsbu.ac.uk/item/866yy

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