Understanding the use of Virtual Reality in Marketing: a text mining-based review

Text mining of industry 4.0 job advertisements

A framework for big data analytics in commercial social networks: A case study on sentiment analysis and fake review detection for marketing decision-making

Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine the state of text mining research by examining the developments within published literature over past years and provide valuable insights for practitioners and researchers on the predominant trends, methods, and applications of text mining research. In accordance with this, more than 200 academic journal articles on the subject are included and discussed in this review; 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, across a broad range of application areas are also investigated. Additionally, the benefits and challenges related to text mining are also briefly outlined.

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Text Mining in Big Data Analytics

Using a probabilistic approach for exploring latent patterns in high-dimensional co-occurrence data, topic models offer researchers a flexible and open framework for soft-clustering large data sets In recent years, there has been a growing interest among marketing scholars and practitioners to adopt topic models in various marketing application domains However, to this date, there is no comprehensive overview of this rapidly evolving field By analyzing a set of 61 published papers along with conceptual contributions, we systematically review this highly heterogeneous area of research In doing so, we 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 Our findings confirm that there is considerable progress done in various marketing sub-areas However, there is still scope for promising future research, in particular with respect to integrating multiple, dynamic data sources, including time-varying covariates and the combination of exploratory topic models with powerful predictive marketing models

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Topic modeling in marketing: recent advances and research opportunities

https://run.unl.pt/bitstream/10362/28033/1/PRita_Research_2018.pdf

Alexandra Amado

Papers

Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis