A cloud-based approach for analyzing viral propagation of linguistic deviations by social networking: Current challenges and pitfalls for text analysis tools

Abstract

Social Networks activities offer rooms a non-trivial testbed for linguistic analysis, introducing significant revolutions into the creation of textual content. Linguistic solecisms, blunders, and generally speaking deviations from standard linguistic norms, are becoming the rule rather than the exception. Social Networks instantly and virally propagate deviations among users, who are increasingly moving away from standard language usage. Performing text analysis on deviated textual documents is a challenging and hard task. In this work, we propose an approach supporting text analysis tasks against a set of deviated textual documents. It exploits a “linguistic blundersonomy”, a taxonomy of linguistic deviations, progressively built by processing textual Big Data provided by social network, in a Cloud-Based environment (SAP-HANA). A preliminary case study for Italian language is presented, showing how the exploitation of a linguistic blundersonomy could improve the precision of a sentiment and opinion mining process, and more generally, of a text analysis process.