Clustering of Social Media Data and Marketing Decisions

Abstract

The technological revolution and the appearance of Social Media have made it possible to generate large volumes of heterogeneous data called Big Data. Today, Big Data Analytics plays a very important role for businesses in making marketing decisions. Social Media Data represents a large part of Big Data and are characterized by complex and unstructured formats, which makes their analysis a difficult task. The challenge for researchers and decision-makers is to find a path to facilitate the analysis of these huge data in order to extract relevant information and to improve marketing decisions and strategies. In this context, previous research proposed several methods and techniques such as Data Mining, visualization and machine learning. Data Mining techniques are among the most widely used techniques and include Clustering techniques. Clustering provides a wide range of techniques that classify unstructured data and detect useful knowledge from large data sets. In this regard, numerous articles on analyzing Social Media Data using Clustering have been published and there has been a rapid increase in the number of publications in the areas of Social Media Data and marketing, in which several Clustering methods have been proposed. Despite this increase, there is a lack of articles organizing these publications according to Clustering techniques and added value. The aim of this paper is to answer the following questions: What are the techniques for aggregating Social Media Data? What are the marketing decisions generated by Social Media Data Clustering? Thus, it will be useful to present a review and a classification of research articles on Social Media Analysis in the field of marketing using Clustering to provide an overview to researchers and managers looking to use these techniques.