Social media data is available at massive volumes, and is often scattered and sometimes incomplete! This is due to the lack of algorithms capable of analyzing this new type of data to benefit from its advantage.
Social Media Data Mining
It is through statistical learning that we have had the ability to embrace the fatal power of this data flow in defining the future. The mining or exploration of social media is a field of research aimed at the extraction and structured representation of possible latent knowledge from a raw flow of social media data [1].
Research and development projects are constantly innovating to develop sophisticated systems with the aim of taking advantage of these massive flow circulating on these platforms (i.e., sentiment and opinion analysis, vulnerability management, integration/extraction of information, management of fake profiles, etc.). And this, by means of new algorithms developed or adaptation of standard algorithms in the field of classic data mining.
Today, collecting and analyzing social media data is no longer an end in itself. It is the way in which this data will be interpreted to predict the future and the ability to be able to anticipate the necessary precautions that is now the ultimate objective [2]. Moreover, nothing is as promising for a company as the forecast of the decisive facts that will come. Since the past cannot be changed, it is then appropriate to anticipate the future and take all necessary precautions to draw it at ease.
Advancement in Social Media Data Mining Projects
Predictive models thus make it possible to detect textual characteristics serving as input data to predict the future – by calculating probabilities that will be providing the data reproduction or change. Indeed, the systems implementing these predictive models have shown impressive results over the years [3].
Research and development projects focusing on the analysis and exploration of data from social networks using sophisticated Machine Learning tools combined with computational linguistics techniques have thus shown a promising future. Indeed, they have guided us towards the discovery of incredible avenues never explored or prospected by traditional Information Extraction despite the challenges presented by these new sources of information.
Companies that are innovating in this sector are likely to be eligible for several funding programs including government grants, and SR&ED.
Sources :
- P. Watson, Exploring social networking: developing critical literacies.
- S. Finlay, Predictive Analytics, Data Mining and Big Data. Myths, Misconceptions and Methods (1st ed.). Basingstoke: Palgrave Macmillan.
- D. Aronson and T. Masters, Predictive-Model Based Trading Systems, Part 1. System Trader Success.
