Comparing data-driven methods for extracting knowledge from user generated content

• Published in: Journal of open innovation Vol. 5; no. 4; pp. 1 - 19
• Main Authors: Saura, José Ramón, Reyes-Menendez, Ana, Filipe, Ferrão
• Format: Journal Article
• Language: English
• Published: Basel MDPI 01.12.2019; Elsevier Ltd; Elsevier Limited; Elsevier
• Subjects: Algorithms; Big Data; business knowledge extraction; Data analysis; Data mining; Datasets; Digital marketing; Dirichlet problem; Hypothesis testing; innovative data-driven methods; Knowledge discovery; Literature reviews; Machine learning; Market strategy; Marketing; Modularity; Research methodology; Scientific visualization; Semantic web; Social networks; Startups; User generated content; innovative data-driven methods; digital marketing; business knowledge extraction
• ISSN: 2199-8531; 2199-8531
• DOI: 10.3390/joitmc5040074

This study aimed to compare two techniques of business knowledge extraction for the identification of insights related to the improvement of digital marketing strategies on a sample of 15,731 tweets. The sample was extracted from user generated content (UGC) from Twitter using two methods based on knowledge extraction techniques for business. In Method 1, an algorithm to detect communities in complex networks was applied; this algorithm, in which we applied data visualization techniques for complex networks analysis, used the modularity of nodes to discover topics. In Method 2, a three-phase process was developed for knowledge extraction that included the application of a latent Dirichlet allocation (LDA) model, a sentiment analysis (SA) that works with machine learning, and a data text mining (DTM) analysis technique. Finally, we compared the results of each of the two techniques to see whether or not the results yielded by these two methods regarding the analysis of companies' digital marketing strategies were mutually complementary.