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Alexandra Amado

Bio: Alexandra Amado is an academic researcher from ISCTE – University Institute of Lisbon. The author has contributed to research in topics: Marketing science & Qualitative marketing research. The author has an hindex of 1, co-authored 1 publications receiving 143 citations.

Papers
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TL;DR: 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.

220 citations


Cited by
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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.

162 citations

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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.

114 citations

Journal ArticleDOI
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.

109 citations

Journal ArticleDOI
16 Jan 2020
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.
Abstract: 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.

103 citations

Journal ArticleDOI
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.
Abstract: 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

71 citations