Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis
TLDR
In this article, the authors present a research literature analysis based on a text mining semi-automated approach with the goal of identifying the main trends in this domain, focusing on relevant terms and topics related with five dimensions: big data, marketing, Geographic location of authors' affiliation (countries and continents), products, and Sectors.About:
This article is published in European Research on Management and Business Economics.The article was published on 2018-01-01 and is currently open access. It has received 220 citations till now. The article focuses on the topics: Marketing science & Marketing research.read more
Citations
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Understanding the use of Virtual Reality in Marketing: a text mining-based review
TL;DR: A text-mining approach using a Bayesian statistical topic model called latent Dirichlet allocation is employed to conduct a comprehensive analysis of 150 articles from 115 journals, revealing seven relevant topics.
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Text mining of industry 4.0 job advertisements
TL;DR: A profile of Industry 4.0 job advertisements is developed using text mining on publicly available job advertisements, which are often used as a channel for collecting relevant information about the required knowledge and skills in rapid-changing industries.
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A framework for big data analytics in commercial social networks: A case study on sentiment analysis and fake review detection for marketing decision-making
TL;DR: This work proposes a framework to automatically analyse these reviews, transforming negative and positive user opinions in a quantitative score, and ranks the best products by price alongside their respective sentiment value and the 5-Star score.
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Text Mining in Big Data Analytics
TL;DR: The state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, are investigated.
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Topic modeling in marketing: recent advances and research opportunities
TL;DR: This work characterize extant contributions employing topic models in marketing along the dimensions data structures and retrieval of input data, implementation and extensions of basic topic models, and model performance evaluation, and confirms that there is considerable progress done in various marketing sub-areas.
References
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Business intelligence and analytics: from big data to big impact
TL;DR: This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A, and introduces and characterized the six articles that comprise this special issue in terms of the proposed BI &A research framework.
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Colin Robson,Kieran McCartan +1 more
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Data-intensive applications, challenges, techniques and technologies: A survey on Big Data
TL;DR: This paper is aimed to demonstrate a close-up view about Big Data, including Big Data applications, Big Data opportunities and challenges, as well as the state-of-the-art techniques and technologies currently adopt to deal with the Big Data problems.
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Text Mining Infrastructure in R
TL;DR: The tm package is presented which provides a framework for text mining applications within R and techniques for count-based analysis methods, text clustering, text classification and string kernels are presented.