The global development of the Internet, which has 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 three 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.

2014) 'Examining retail customer experience and the moderation eect of loyalty programmes.', International journal of retail and distribution management., 42 (10). pp. 929-947. Further information on publisher's website:https://doi.org/10.1108/ijrdm-11-2013-0208Publisher's copyright statement:This article is c Emerald Group Publishing and permission has been granted for this version to appear here http://dro.dur.ac.uk/23485. Emerald does not grant permission for this article to be further copied/distributed or hosted elsewhere without the express permission from Emerald Group Publishing Limited Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details.

Scientific knowledge builds by continuously subjecting its known laws to differentiated replications. Empirical generalisations capturing the Law of Double Jeopardy have been extensively tested in this way for decades, and rightly so because they continue to provide a valuable managerial key to the multi-million dollar question of how brands grow. This research continues that work, first by extending knowledge of the operation of Double Jeopardy in the less familiar conditions of long-run continuous buying, emerging markets, capital purchasing and house of brand strategies, and second by validating the rather overlooked w(1- b) approximation as a simple tool to predict behavioural brand loyalty. Observations of competitive brand performance in 32 differentiated replications, some over thirty five years apart, find no boundary condition to the operation of the Double Jeopardy characteristic even in contexts that might initially suggest a challenge to its independence assumptions. We outline the implications for managers in these new findings in terms of insight, planning and brand audit.

New analysis and visualization techniques are required to glean useful insights from the vast amounts of data generated by new technologies and data sharing platforms. The aim of this article is to lay a foundation for such techniques so that the age of big data may also be the age of knowledge, visualization, and understanding. Education is the keystone area used in this study because it is deeply affected by digital platforms as an educational medium and also because it deals mostly with digital natives who use information and communication technology (ICT) for all manner of purposes. Students and teachers are therefore a rich source of user generated content (UGC) on social networks and digital platforms. This article shows how useful knowledge can be extracted and visualized from samples of readily available UGC, in this case the text published in tweets from the social network Twitter. The first stage employs topic-modeling using LDA (latent dirichlet allocation) to identify topics, which are then subjected to sentiment analysis (SA) using machine-learning (developed in Python). The results take on meaning through an application of data mining techniques and a data visualization algorithm for complex networks. The results obtained show insights related to innovative educational trends that practitioners can use to improve strategies and interventions in the education sector in a short-term future.

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